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mining</title> <meta name="description" content="Search results for: process mining"> <meta name="keywords" content="process mining"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="process mining"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 16020</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: process mining</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16020</span> Object-Centric Process Mining Using Process Cubes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anahita%20Farhang%20Ghahfarokhi">Anahita Farhang Ghahfarokhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Alessandro%20Berti"> Alessandro Berti</a>, <a href="https://publications.waset.org/abstracts/search?q=Wil%20M.P.%20van%20der%20Aalst"> Wil M.P. van der Aalst</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Process mining provides ways to analyze business processes. Common process mining techniques consider the process as a whole. However, in real-life business processes, different behaviors exist that make the overall process too complex to interpret. Process comparison is a branch of process mining that isolates different behaviors of the process from each other by using process cubes. Process cubes organize event data using different dimensions. Each cell contains a set of events that can be used as an input to apply process mining techniques. Existing work on process cubes assume single case notions. However, in real processes, several case notions (e.g., order, item, package, etc.) are intertwined. Object-centric process mining is a new branch of process mining addressing multiple case notions in a process. To make a bridge between object-centric process mining and process comparison, we propose a process cube framework, which supports process cube operations such as slice and dice on object-centric event logs. To facilitate the comparison, the framework is integrated with several object-centric process discovery approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20process%20mining" title="multidimensional process mining">multidimensional process mining</a>, <a href="https://publications.waset.org/abstracts/search?q=mMulti-perspective%20business%20processes" title=" mMulti-perspective business processes"> mMulti-perspective business processes</a>, <a href="https://publications.waset.org/abstracts/search?q=OLAP" title=" OLAP"> OLAP</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20cubes" title=" process cubes"> process cubes</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20discovery" title=" process discovery"> process discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20mining" title=" process mining"> process mining</a> </p> <a href="https://publications.waset.org/abstracts/131006/object-centric-process-mining-using-process-cubes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131006.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">255</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16019</span> Block Mining: Block Chain Enabled Process Mining Database</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=James%20Newman">James Newman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Process mining is an emerging technology that looks to serialize enterprise data in time series data. It has been used by many companies and has been the subject of a variety of research papers. However, the majority of current efforts have looked at how to best create process mining from standard relational databases. This paper is the first pass at outlining a database custom-built for the minimal viable product of process mining. We present Block Miner, a blockchain protocol to store process mining data across a distributed network. We demonstrate the feasibility of storing process mining data on the blockchain. We present a proof of concept and show how the intersection of these two technologies helps to solve a variety of issues, including but not limited to ransomware attacks, tax documentation, and conflict resolution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blockchain" title="blockchain">blockchain</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20mining" title=" process mining"> process mining</a>, <a href="https://publications.waset.org/abstracts/search?q=memory%20optimization" title=" memory optimization"> memory optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=protocol" title=" protocol"> protocol</a> </p> <a href="https://publications.waset.org/abstracts/166846/block-mining-block-chain-enabled-process-mining-database" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166846.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">102</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16018</span> Mining Diagnostic Investigation Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sohail%20Imran">Sohail Imran</a>, <a href="https://publications.waset.org/abstracts/search?q=Tariq%20Mahmood"> Tariq Mahmood</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In complex healthcare diagnostic investigation process, medical practitioners have to focus on ways to standardize their processes to perform high quality care and optimize the time and costs. Process mining techniques can be applied to extract process related knowledge from data without considering causal and dynamic dependencies in business domain and processes. The application of process mining is effective in diagnostic investigation. It is very helpful where a treatment gives no dispositive evidence favoring it. In this paper, we applied process mining to discover important process flow of diagnostic investigation for hepatitis patients. This approach has some benefits which can enhance the quality and efficiency of diagnostic investigation processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20mining" title="process mining">process mining</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare" title=" healthcare"> healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnostic%20investigation%20process" title=" diagnostic investigation process"> diagnostic investigation process</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20flow" title=" process flow"> process flow</a> </p> <a href="https://publications.waset.org/abstracts/9370/mining-diagnostic-investigation-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9370.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">523</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16017</span> A Review Paper on Data Mining and Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sikander%20Singh%20Cheema">Sikander Singh Cheema</a>, <a href="https://publications.waset.org/abstracts/search?q=Jasmeen%20Kaur"> Jasmeen Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the concept of data mining is summarized and its one of the important process i.e KDD is summarized. The data mining based on Genetic Algorithm is researched in and ways to achieve the data mining Genetic Algorithm are surveyed. This paper also conducts a formal review on the area of data mining tasks and genetic algorithm in various fields. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=KDD" title=" KDD"> KDD</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=descriptive%20mining" title=" descriptive mining"> descriptive mining</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20mining" title=" predictive mining"> predictive mining</a> </p> <a href="https://publications.waset.org/abstracts/43637/a-review-paper-on-data-mining-and-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43637.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">591</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16016</span> Towards a Distributed Computation Platform Tailored for Educational Process Discovery and Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Awatef%20Hicheur%20Cairns">Awatef Hicheur Cairns</a>, <a href="https://publications.waset.org/abstracts/search?q=Billel%20Gueni"> Billel Gueni</a>, <a href="https://publications.waset.org/abstracts/search?q=Hind%20Hafdi"> Hind Hafdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Christian%20Joubert"> Christian Joubert</a>, <a href="https://publications.waset.org/abstracts/search?q=Nasser%20Khelifa"> Nasser Khelifa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given the ever changing needs of the job markets, education and training centers are increasingly held accountable for student success. Therefore, education and training centers have to focus on ways to streamline their offers and educational processes in order to achieve the highest level of quality in curriculum contents and managerial decisions. Educational process mining is an emerging field in the educational data mining (EDM) discipline, concerned with developing methods to discover, analyze and provide a visual representation of complete educational processes. In this paper, we present our distributed computation platform which allows different education centers and institutions to load their data and access to advanced data mining and process mining services. To achieve this, we present also a comparative study of the different clustering techniques developed in the context of process mining to partition efficiently educational traces. Our goal is to find the best strategy for distributing heavy analysis computations on many processing nodes of our platform. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=educational%20process%20mining" title="educational process mining">educational process mining</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20process%20mining" title=" distributed process mining"> distributed process mining</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20platform" title=" distributed platform"> distributed platform</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20data%20mining" title=" educational data mining"> educational data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=ProM" title=" ProM"> ProM</a> </p> <a href="https://publications.waset.org/abstracts/27675/towards-a-distributed-computation-platform-tailored-for-educational-process-discovery-and-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27675.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">454</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16015</span> Concept Drifts Detection and Localisation in Process Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20V.%20Manoj%20Kumar">M. V. Manoj Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Likewin%20Thomas"> Likewin Thomas</a>, <a href="https://publications.waset.org/abstracts/search?q=Annappa"> Annappa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Process mining provides methods and techniques for analyzing event logs recorded in modern information systems that support real-world operations. While analyzing an event-log, state-of-the-art techniques available in process mining believe that the operational process as a static entity (stationary). This is not often the case due to the possibility of occurrence of a phenomenon called concept drift. During the period of execution, the process can experience concept drift and can evolve with respect to any of its associated perspectives exhibiting various patterns-of-change with a different pace. Work presented in this paper discusses the main aspects to consider while addressing concept drift phenomenon and proposes a method for detecting and localizing the sudden concept drifts in control-flow perspective of the process by using features extracted by processing the traces in the process log. Our experimental results are promising in the direction of efficiently detecting and localizing concept drift in the context of process mining research discipline. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=abrupt%20drift" title="abrupt drift">abrupt drift</a>, <a href="https://publications.waset.org/abstracts/search?q=concept%20drift" title=" concept drift"> concept drift</a>, <a href="https://publications.waset.org/abstracts/search?q=sudden%20drift" title=" sudden drift"> sudden drift</a>, <a href="https://publications.waset.org/abstracts/search?q=control-flow%20perspective" title=" control-flow perspective"> control-flow perspective</a>, <a href="https://publications.waset.org/abstracts/search?q=detection%20and%20localization" title=" detection and localization"> detection and localization</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20mining" title=" process mining"> process mining</a> </p> <a href="https://publications.waset.org/abstracts/44971/concept-drifts-detection-and-localisation-in-process-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44971.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">345</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16014</span> A Modular Framework for Enabling Analysis for Educators with Different Levels of Data Mining Skills</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kyle%20De%20Freitas">Kyle De Freitas</a>, <a href="https://publications.waset.org/abstracts/search?q=Margaret%20Bernard"> Margaret Bernard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Enabling data mining analysis among a wider audience of educators is an active area of research within the educational data mining (EDM) community. The paper proposes a framework for developing an environment that caters for educators who have little technical data mining skills as well as for more advanced users with some data mining expertise. This framework architecture was developed through the review of the strengths and weaknesses of existing models in the literature. The proposed framework provides a modular architecture for future researchers to focus on the development of specific areas within the EDM process. Finally, the paper also highlights a strategy of enabling analysis through either the use of predefined questions or a guided data mining process and highlights how the developed questions and analysis conducted can be reused and extended over time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=educational%20data%20mining" title="educational data mining">educational data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20management%20system" title=" learning management system"> learning management system</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title=" learning analytics"> learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=EDM%20framework" title=" EDM framework"> EDM framework</a> </p> <a href="https://publications.waset.org/abstracts/78786/a-modular-framework-for-enabling-analysis-for-educators-with-different-levels-of-data-mining-skills" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78786.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">326</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16013</span> Data-Mining Approach to Analyzing Industrial Process Information for Real-Time Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seung-Lock%20Seo">Seung-Lock Seo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents a data-mining empirical monitoring scheme for industrial processes with partially unbalanced data. Measurement data of good operations are relatively easy to gather, but in unusual special events or faults it is generally difficult to collect process information or almost impossible to analyze some noisy data of industrial processes. At this time some noise filtering techniques can be used to enhance process monitoring performance in a real-time basis. In addition, pre-processing of raw process data is helpful to eliminate unwanted variation of industrial process data. In this work, the performance of various monitoring schemes was tested and demonstrated for discrete batch process data. It showed that the monitoring performance was improved significantly in terms of monitoring success rate of given process faults. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20data" title=" process data"> process data</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=safety" title=" safety"> safety</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20processes" title=" industrial processes"> industrial processes</a> </p> <a href="https://publications.waset.org/abstracts/3929/data-mining-approach-to-analyzing-industrial-process-information-for-real-time-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3929.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">401</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16012</span> Trace Logo: A Notation for Representing Control-Flow of Operational Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20V.%20Manoj%20Kumar">M. V. Manoj Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Likewin%20Thomas"> Likewin Thomas</a>, <a href="https://publications.waset.org/abstracts/search?q=Annappa"> Annappa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Process mining research discipline bridges the gap between data mining and business process modeling and analysis, it offers the process-centric and end-to-end methods/techniques for analyzing information of real-world process detailed in operational event-logs. In this paper, we have proposed a notation called trace logo for graphically representing control-flow perspective (order of execution of activities) of process. A trace logo consists of a stack of activity names at each position, sizes of the activity name indicates their frequency in the traces and the total height of the activity depicts the information content of the position. A trace logo created from a set of aligned traces generated using Multiple Trace Alignment technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=consensus%20trace" title="consensus trace">consensus trace</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20mining" title=" process mining"> process mining</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20trace%20alignment" title=" multiple trace alignment"> multiple trace alignment</a>, <a href="https://publications.waset.org/abstracts/search?q=trace%20logo" title=" trace logo "> trace logo </a> </p> <a href="https://publications.waset.org/abstracts/44978/trace-logo-a-notation-for-representing-control-flow-of-operational-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44978.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16011</span> Performance Evaluation of Production Schedules Based on Process Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kwan%20Hee%20Han">Kwan Hee Han</a> </p> <p class="card-text"><strong>Abstract:</strong></p> External environment of enterprise is rapidly changing majorly by global competition, cost reduction pressures, and new technology. In these situations, production scheduling function plays a critical role to meet customer requirements and to attain the goal of operational efficiency. It deals with short-term decision making in the production process of the whole supply chain. The major task of production scheduling is to seek a balance between customer orders and limited resources. In manufacturing companies, this task is so difficult because it should efficiently utilize resource capacity under the careful consideration of many interacting constraints. At present, many computerized software solutions have been utilized in many enterprises to generate a realistic production schedule to overcome the complexity of schedule generation. However, most production scheduling systems do not provide sufficient information about the validity of the generated schedule except limited statistics. Process mining only recently emerged as a sub-discipline of both data mining and business process management. Process mining techniques enable the useful analysis of a wide variety of processes such as process discovery, conformance checking, and bottleneck analysis. In this study, the performance of generated production schedule is evaluated by mining event log data of production scheduling software system by using the process mining techniques since every software system generates event logs for the further use such as security investigation, auditing and error bugging. An application of process mining approach is proposed for the validation of the goodness of production schedule generated by scheduling software systems in this study. By using process mining techniques, major evaluation criteria such as utilization of workstation, existence of bottleneck workstations, critical process route patterns, and work load balance of each machine over time are measured, and finally, the goodness of production schedule is evaluated. By using the proposed process mining approach for evaluating the performance of generated production schedule, the quality of production schedule of manufacturing enterprises can be improved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=event%20log" title=" event log"> event log</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20mining" title=" process mining"> process mining</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20scheduling" title=" production scheduling"> production scheduling</a> </p> <a href="https://publications.waset.org/abstracts/74583/performance-evaluation-of-production-schedules-based-on-process-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74583.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16010</span> Data Mining As A Tool For Knowledge Management: A Review </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maram%20Saleh">Maram Saleh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Knowledge has become an essential resource in today’s economy and become the most important asset of maintaining competition advantage in organizations. The importance of knowledge has made organizations to manage their knowledge assets and resources through all multiple knowledge management stages such as: Knowledge Creation, knowledge storage, knowledge sharing and knowledge use. Researches on data mining are continues growing over recent years on both business and educational fields. Data mining is one of the most important steps of the knowledge discovery in databases process aiming to extract implicit, unknown but useful knowledge and it is considered as significant subfield in knowledge management. Data miming have the great potential to help organizations to focus on extracting the most important information on their data warehouses. Data mining tools and techniques can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This review paper explores the applications of data mining techniques in supporting knowledge management process as an effective knowledge discovery technique. In this paper, we identify the relationship between data mining and knowledge management, and then focus on introducing some application of date mining techniques in knowledge management for some real life domains. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Data%20Mining" title="Data Mining">Data Mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Knowledge%20management" title=" Knowledge management"> Knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=Knowledge%20discovery" title=" Knowledge discovery"> Knowledge discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=Knowledge%20creation." title=" Knowledge creation."> Knowledge creation.</a> </p> <a href="https://publications.waset.org/abstracts/137030/data-mining-as-a-tool-for-knowledge-management-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137030.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">208</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16009</span> Indexing and Incremental Approach Using Map Reduce Bipartite Graph (MRBG) for Mining Evolving Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adarsh%20Shroff">Adarsh Shroff</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big data is a collection of dataset so large and complex that it becomes difficult to process using data base management tools. To perform operations like search, analysis, visualization on big data by using data mining; which is the process of extraction of patterns or knowledge from large data set. In recent years, the data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. This project uses i2MapReduce, an incremental processing extension to Map Reduce, the most widely used framework for mining big data. I2MapReduce performs key-value pair level incremental processing rather than task level re-computation, supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. To optimize the mining results, evaluate i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics for efficient mining. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=map%20reduce" title=" map reduce"> map reduce</a>, <a href="https://publications.waset.org/abstracts/search?q=incremental%20processing" title=" incremental processing"> incremental processing</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20computation" title=" iterative computation"> iterative computation</a> </p> <a href="https://publications.waset.org/abstracts/46413/indexing-and-incremental-approach-using-map-reduce-bipartite-graph-mrbg-for-mining-evolving-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46413.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">350</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16008</span> Mining Big Data in Telecommunications Industry: Challenges, Techniques, and Revenue Opportunity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hoda%20A.%20Abdel%20Hafez">Hoda A. Abdel Hafez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mining%20big%20data" title="mining big data">mining big data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=telecommunication" title=" telecommunication"> telecommunication</a> </p> <a href="https://publications.waset.org/abstracts/41412/mining-big-data-in-telecommunications-industry-challenges-techniques-and-revenue-opportunity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41412.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">410</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16007</span> Process Mining as an Ecosystem Platform to Mitigate a Deficiency of Processes Modelling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yusra%20Abdulsalam%20Alqamati">Yusra Abdulsalam Alqamati</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Alkilany"> Ahmed Alkilany</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The teaching staff is a distinct group whose impact is on the educational process and which plays an important role in enhancing the quality of the academic education process. To improve the management effectiveness of the academy, the Teaching Staff Management System (TSMS) proposes that all teacher processes be digitized. Since the BPMN approach can accurately describe the processes, it lacks a clear picture of the process flow map, something that the process mining approach has, which is extracting information from event logs for discovery, monitoring, and model enhancement. Therefore, these two methodologies were combined to create the most accurate representation of system operations, the ability to extract data records and mining processes, recreate them in the form of a Petri net, and then generate them in a BPMN model for a more in-depth view of process flow. Additionally, the TSMS processes will be orchestrated to handle all requests in a guaranteed small-time manner thanks to the integration of the Google Cloud Platform (GCP), the BPM engine, and allowing business owners to take part throughout the entire TSMS project development lifecycle. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20mining" title="process mining">process mining</a>, <a href="https://publications.waset.org/abstracts/search?q=BPM" title=" BPM"> BPM</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20process%20model%20and%20notation" title=" business process model and notation"> business process model and notation</a>, <a href="https://publications.waset.org/abstracts/search?q=Petri%20net" title=" Petri net"> Petri net</a>, <a href="https://publications.waset.org/abstracts/search?q=teaching%20staff" title=" teaching staff"> teaching staff</a>, <a href="https://publications.waset.org/abstracts/search?q=Google%20Cloud%20Platform" title=" Google Cloud Platform"> Google Cloud Platform</a> </p> <a href="https://publications.waset.org/abstracts/160668/process-mining-as-an-ecosystem-platform-to-mitigate-a-deficiency-of-processes-modelling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160668.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16006</span> Application Potential of Forward Osmosis-Nanofiltration Hybrid Process for the Treatment of Mining Waste Water</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ketan%20Mahawer">Ketan Mahawer</a>, <a href="https://publications.waset.org/abstracts/search?q=Abeer%20Mutto"> Abeer Mutto</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20K.%20Gupta"> S. K. Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The mining wastewater contains inorganic metal salts, which makes it saline and additionally contributes to contaminating the surface and underground freshwater reserves that exist nearby mineral processing industries. Therefore, treatment of wastewater and water recovery is obligatory by any available technology before disposing it into the environment. Currently, reverse osmosis (RO) is the commercially acceptable conventional membrane process for saline wastewater treatment, but consumes an enormous amount of energy and makes the process expensive. To solve this industrial problem with minimum energy consumption, we tested the feasibility of forward osmosis-nanofiltration (FO-NF) hybrid process for the mining wastewater treatment. The FO-NF process experimental results for 0.029M concentration of saline wastewater treated by 0.42 M sodium-sulfate based draw solution shows that specific energy consumption of the FO-NF process compared with standalone NF was slightly above (between 0.5-1 kWh/m3) from conventional process. However, average freshwater recovery was 30% more from standalone NF with same feed and operating conditions. Hence, FO-NF process in place of RO/NF offers a huge possibility for treating mining industry wastewater and concentrates the metals as the by-products without consuming an excessive/large amount of energy and in addition, mitigates the fouling in long periods of treatment, which also decreases the maintenance and replacement cost of the separation process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forward%20osmosis" title="forward osmosis">forward osmosis</a>, <a href="https://publications.waset.org/abstracts/search?q=nanofiltration" title=" nanofiltration"> nanofiltration</a>, <a href="https://publications.waset.org/abstracts/search?q=mining" title=" mining"> mining</a>, <a href="https://publications.waset.org/abstracts/search?q=draw%20solution" title=" draw solution"> draw solution</a>, <a href="https://publications.waset.org/abstracts/search?q=divalent%20solute" title=" divalent solute"> divalent solute</a> </p> <a href="https://publications.waset.org/abstracts/148367/application-potential-of-forward-osmosis-nanofiltration-hybrid-process-for-the-treatment-of-mining-waste-water" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148367.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">118</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16005</span> Healthcare Data Mining Innovations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eugenia%20Jilinguirian">Eugenia Jilinguirian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the healthcare industry, data mining is essential since it transforms the field by collecting useful data from large datasets. Data mining is the process of applying advanced analytical methods to large patient records and medical histories in order to identify patterns, correlations, and trends. Healthcare professionals can improve diagnosis accuracy, uncover hidden linkages, and predict disease outcomes by carefully examining these statistics. Additionally, data mining supports personalized medicine by personalizing treatment according to the unique attributes of each patient. This proactive strategy helps allocate resources more efficiently, enhances patient care, and streamlines operations. However, to effectively apply data mining, however, and ensure the use of private healthcare information, issues like data privacy and security must be carefully considered. Data mining continues to be vital for searching for more effective, efficient, and individualized healthcare solutions as technology evolves. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare" title=" healthcare"> healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=individualised%20healthcare" title=" individualised healthcare"> individualised healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare%20solutions" title=" healthcare solutions"> healthcare solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=database" title=" database"> database</a> </p> <a href="https://publications.waset.org/abstracts/178640/healthcare-data-mining-innovations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178640.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">66</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16004</span> Business Intelligence for Profiling of Telecommunication Customer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rokhmatul%20Insani">Rokhmatul Insani</a>, <a href="https://publications.waset.org/abstracts/search?q=Hira%20Laksmiwati%20Soemitro"> Hira Laksmiwati Soemitro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Business Intelligence is a methodology that exploits the data to produce information and knowledge systematically, business intelligence can support the decision-making process. Some methods in business intelligence are data warehouse and data mining. A data warehouse can store historical data from transactional data. For data modelling in data warehouse, we apply dimensional modelling by Kimball. While data mining is used to extracting patterns from the data and get insight from the data. Data mining has many techniques, one of which is segmentation. For profiling of telecommunication customer, we use customer segmentation according to customer’s usage of services, customer invoice and customer payment. Customers can be grouped according to their characteristics and can be identified the profitable customers. We apply K-Means Clustering Algorithm for segmentation. The input variable for that algorithm we use RFM (Recency, Frequency and Monetary) model. All process in data mining, we use tools IBM SPSS modeller. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title="business intelligence">business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20segmentation" title=" customer segmentation"> customer segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20warehouse" title=" data warehouse"> data warehouse</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a> </p> <a href="https://publications.waset.org/abstracts/46969/business-intelligence-for-profiling-of-telecommunication-customer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46969.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">484</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16003</span> Frequent Itemset Mining Using Rough-Sets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Usman%20Qamar">Usman Qamar</a>, <a href="https://publications.waset.org/abstracts/search?q=Younus%20Javed"> Younus Javed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and rough-sets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rough-sets" title="rough-sets">rough-sets</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=outliers" title=" outliers"> outliers</a>, <a href="https://publications.waset.org/abstracts/search?q=frequent%20itemset%20mining" title=" frequent itemset mining"> frequent itemset mining</a> </p> <a href="https://publications.waset.org/abstracts/14372/frequent-itemset-mining-using-rough-sets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14372.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">437</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16002</span> Project Risk Assessment of the Mining Industry of Ghana</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Charles%20Amoatey">Charles Amoatey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The issue of risk in the mining industry is a global phenomenon and the Ghanaian mining industry is not exempted. The main purpose of this study is to identify the critical risk factors affecting the mining industry. The study takes an integrated view of the mining industry by examining the contribution of various risk factors to mining project failure in Ghana. A questionnaire survey was conducted to solicit the critical risk factors from key mining practitioners. About 80 respondents from 11 mining firms participated in the survey. The study identified 22 risk factors contributing to mining project failure in Ghana. The five most critical risk factors based on both probability of occurrence and impact were: (1) unstable commodity prices, (2) inflation/exchange rate, (3) land degradation, (4) high cost of living and (5) government bureaucracy for obtaining licenses. Furthermore, the study found that risk assessment in the mining sector has a direct link with mining project sustainability. Mitigation measures for addressing the identified risk factors were discussed. The key findings emphasize the need for a comprehensive risk management culture in the entire mining industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=risk" title="risk">risk</a>, <a href="https://publications.waset.org/abstracts/search?q=assessment" title=" assessment"> assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=mining" title=" mining"> mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghana" title=" Ghana"> Ghana</a> </p> <a href="https://publications.waset.org/abstracts/48909/project-risk-assessment-of-the-mining-industry-of-ghana" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48909.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">452</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16001</span> A Comprehensive Survey and Improvement to Existing Privacy Preserving Data Mining Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tosin%20Ige">Tosin Ige</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ethics must be a condition of the world, like logic. (Ludwig Wittgenstein, 1889-1951). As important as data mining is, it possess a significant threat to ethics, privacy, and legality, since data mining makes it difficult for an individual or consumer (in the case of a company) to control the accessibility and usage of his data. This research focuses on Current issues and the latest research and development on Privacy preserving data mining methods as at year 2022. It also discusses some advances in those techniques while at the same time highlighting and providing a new technique as a solution to an existing technique of privacy preserving data mining methods. This paper also bridges the wide gap between Data mining and the Web Application Programing Interface (web API), where research is urgently needed for an added layer of security in data mining while at the same time introducing a seamless and more efficient way of data mining. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data" title="data">data</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy" title=" privacy"> privacy</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=association%20rule" title=" association rule"> association rule</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20preserving" title=" privacy preserving"> privacy preserving</a>, <a href="https://publications.waset.org/abstracts/search?q=mining%20technique" title=" mining technique"> mining technique</a> </p> <a href="https://publications.waset.org/abstracts/145870/a-comprehensive-survey-and-improvement-to-existing-privacy-preserving-data-mining-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145870.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">173</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16000</span> Analysis of Reliability of Mining Shovel Using Weibull Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anurag%20Savarnya">Anurag Savarnya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The reliability of the various parts of electric mining shovel has been assessed through the application of Weibull Model. The study was initiated to find reliability of components of electric mining shovel. The paper aims to optimize the reliability of components and increase the life cycle of component. A multilevel decomposition of the electric mining shovel was done and maintenance records were used to evaluate the failure data and appropriate system characterization was done to model the system in terms of reasonable number of components. The approach used develops a mathematical model to assess the reliability of the electric mining shovel components. The model can be used to predict reliability of components of the hydraulic mining shovel and system performance. Reliability is an inherent attribute to a system. When the life-cycle costs of a system are being analyzed, reliability plays an important role as a major driver of these costs and has considerable influence on system performance. It is an iterative process that begins with specification of reliability goals consistent with cost and performance objectives. The data were collected from an Indian open cast coal mine and the reliability of various components of the electric mining shovel has been assessed by following a Weibull Model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reliability" title="reliability">reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=Weibull%20model" title=" Weibull model"> Weibull model</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20mining%20shovel" title=" electric mining shovel"> electric mining shovel</a> </p> <a href="https://publications.waset.org/abstracts/8913/analysis-of-reliability-of-mining-shovel-using-weibull-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8913.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">514</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15999</span> An Adaptive Distributed Incremental Association Rule Mining System </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adewale%20O.%20Ogunde">Adewale O. Ogunde</a>, <a href="https://publications.waset.org/abstracts/search?q=Olusegun%20Folorunso"> Olusegun Folorunso</a>, <a href="https://publications.waset.org/abstracts/search?q=Adesina%20S.%20Sodiya"> Adesina S. Sodiya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most existing Distributed Association Rule Mining (DARM) systems are still facing several challenges. One of such challenges that have not received the attention of many researchers is the inability of existing systems to adapt to constantly changing databases and mining environments. In this work, an Adaptive Incremental Mining Algorithm (AIMA) is therefore proposed to address these problems. AIMA employed multiple mobile agents for the entire mining process. AIMA was designed to adapt to changes in the distributed databases by mining only the incremental database updates and using this to update the existing rules in order to improve the overall response time of the DARM system. In AIMA, global association rules were integrated incrementally from one data site to another through Results Integration Coordinating Agents. The mining agents in AIMA were made adaptive by defining mining goals with reasoning and behavioral capabilities and protocols that enabled them to either maintain or change their goals. AIMA employed Java Agent Development Environment Extension for designing the internal agents’ architecture. Results from experiments conducted on real datasets showed that the adaptive system, AIMA performed better than the non-adaptive systems with lower communication costs and higher task completion rates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptivity" title="adaptivity">adaptivity</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20association%20rule%20mining" title=" distributed association rule mining"> distributed association rule mining</a>, <a href="https://publications.waset.org/abstracts/search?q=incremental%20mining" title=" incremental mining"> incremental mining</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20agents" title=" mobile agents"> mobile agents</a> </p> <a href="https://publications.waset.org/abstracts/10014/an-adaptive-distributed-incremental-association-rule-mining-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10014.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">393</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15998</span> Reclamation of Mining Using Vegetation - A Comparative Study of Open Pit Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Surendra%20Babu">G. Surendra Babu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We all know the importance of mineral wealth, which has been buried inside the layers of the earth for decades. These are the natural energy sources that are used in our day to day life like fuel, electricity, construction, etc. but the process of extraction causes damage to the nature that can’t be returned back and which are left over after completion of mining we can see these are barren from decades these remain unused degraded land. Most of them are covered with vegetation before the start during mining which damages the native vegetation of the region and disturbs the watershed boundary of the regions and it also disturbs the biodiversity of the reign. The major motto of the study is to understand the various issues that are found and to understand various methods of reclamations process that are suitable for revegetating and also variously practiced which are carried out in the different case studies and government guidelines procedure of lease licenses which includes the environmental clearances and also to study the vegetation pattern according to the major issues identified. And finally suggesting the new guidelines with respect to the old guidelines which helps in the revegetation of the mine-sites which helps in establishing of its own sustainable ecosystem in future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reclamation" title="reclamation">reclamation</a>, <a href="https://publications.waset.org/abstracts/search?q=open-pit%20mining" title=" open-pit mining"> open-pit mining</a>, <a href="https://publications.waset.org/abstracts/search?q=revegetation" title=" revegetation"> revegetation</a>, <a href="https://publications.waset.org/abstracts/search?q=reclamation%20methods" title=" reclamation methods"> reclamation methods</a> </p> <a href="https://publications.waset.org/abstracts/145658/reclamation-of-mining-using-vegetation-a-comparative-study-of-open-pit-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145658.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">193</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15997</span> Association Rules Mining Task Using Metaheuristics: Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abir%20Derouiche">Abir Derouiche</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdesslem%20Layeb"> Abdesslem Layeb </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Association Rule Mining (ARM) is one of the most popular data mining tasks and it is widely used in various areas. The search for association rules is an NP-complete problem that is why metaheuristics have been widely used to solve it. The present paper presents the ARM as an optimization problem and surveys the proposed approaches in the literature based on metaheuristics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Optimization" title="Optimization">Optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Metaheuristics" title=" Metaheuristics"> Metaheuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=Data%20Mining" title=" Data Mining"> Data Mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Association%20rules%20Mining" title=" Association rules Mining"> Association rules Mining</a> </p> <a href="https://publications.waset.org/abstracts/120254/association-rules-mining-task-using-metaheuristics-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/120254.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">159</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15996</span> Study for Establishing a Concept of Underground Mining in a Folded Deposit with Weathering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chandan%20Pramanik">Chandan Pramanik</a>, <a href="https://publications.waset.org/abstracts/search?q=Bikramjit%20Chanda"> Bikramjit Chanda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Large metal mines operated with open-cast mining methods must transition to underground mining at the conclusion of the operation; however, this requires a period of a difficult time when production convergence due to interference between the two mining methods. A transition model with collaborative mining operations is presented and established in this work, based on the case of the South Kaliapani Underground Project, to address these technical issues of inadequate production security and other mining challenges during the transition phase and beyond. By integrating the technology of the small-scale Drift and Fill method and Highly productive Sub Level Open Stoping at deep section, this hybrid mining concept tries to eliminate major bottlenecks and offers an optimized production profile with the safe and sustainable operation. Considering every geo-mining aspect, this study offers a genuine and precise technical deliberation for the transition from open pit to underground mining. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drift%20and%20fill" title="drift and fill">drift and fill</a>, <a href="https://publications.waset.org/abstracts/search?q=geo-mining%20aspect" title=" geo-mining aspect"> geo-mining aspect</a>, <a href="https://publications.waset.org/abstracts/search?q=sublevel%20open%20stoping" title=" sublevel open stoping"> sublevel open stoping</a>, <a href="https://publications.waset.org/abstracts/search?q=underground%20mining%20method" title=" underground mining method"> underground mining method</a> </p> <a href="https://publications.waset.org/abstracts/160119/study-for-establishing-a-concept-of-underground-mining-in-a-folded-deposit-with-weathering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160119.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">100</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15995</span> The Environmental and Socio Economic Impacts of Mining on Local Livelihood in Cameroon: A Case Study in Bertoua</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fongang%20Robert%20Tichuck">Fongang Robert Tichuck</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper reports the findings of a study undertaken to assess the socio-economic and environmental impacts of mining in Bertoua Eastern Region of Cameroon. In addition to sampling community perceptions of mining activities, the study prescribes interventions that can assist in mitigating the negative impacts of mining. Marked environmental and interrelated socio-economic improvements can be achieved within regional artisanal gold mines if the government provides technical support to local operators, regulations are improved, and illegal mining activity is reduced. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gold%20mining" title="gold mining">gold mining</a>, <a href="https://publications.waset.org/abstracts/search?q=socio-economic" title=" socio-economic"> socio-economic</a>, <a href="https://publications.waset.org/abstracts/search?q=mining%20activities" title=" mining activities"> mining activities</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20people" title=" local people"> local people</a> </p> <a href="https://publications.waset.org/abstracts/42339/the-environmental-and-socio-economic-impacts-of-mining-on-local-livelihood-in-cameroon-a-case-study-in-bertoua" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42339.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">396</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15994</span> Identification of Environmental Damage Due to Mining Area Bangka Islands in Indonesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aroma%20Elmina%20Martha">Aroma Elmina Martha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Environment affects the continuity of life and human well-being and the bodies of other living. Environmental quality is very closely related to the quality of life. Sustainability must be protected from damage due to the use of natural resources, such as tin mining in Bangka island. This research is a descriptive study, which identifies the environmental damage caused by mining land and sea in Bangka district. The approach used is juridical, social and economic. The study uses primary legal materials, secondary, and tertiary, equipped with field research. The analysis technique used is qualitative analysis. The impacts of mining on land among other physical and chemical damage, erosion and widening the depth of the river, a pool of micro-climate, the quality and feasibility, vegetation, wildlife and biodiversity, land values, social and economic. This mining causes damage to the soil structure, and puddles in the former digs which were not backfilled again. The impact of mining on the ocean such as changes in current surge, erosion and abrasion basic coastal waters, shoreline change, marine water quality changes, and changes in marine communities. The findings of the research show that tin mining in the sea also potentially have a significant impact on the life of the reef, populations of marine organisms. However, mining on land needs to consider the impact of the damage, so that the damage can be minimized. In the recovery process needs to be pursued by exploiting the rest of the pile of tin. Thus, mining activities should take into account the distance of beach sediment size, wave height, wave length, wave period, and the acceleration of gravity. The process of the tin washing should be done in a fairly safe area, thus avoiding damage to the coral reefs that will eventually reduce the population of marine life. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=abration" title="abration">abration</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20damage" title=" environmental damage"> environmental damage</a>, <a href="https://publications.waset.org/abstracts/search?q=mining" title=" mining"> mining</a>, <a href="https://publications.waset.org/abstracts/search?q=shoreline" title=" shoreline"> shoreline</a> </p> <a href="https://publications.waset.org/abstracts/61355/identification-of-environmental-damage-due-to-mining-area-bangka-islands-in-indonesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61355.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">322</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15993</span> Analysis of Changes Being Done of the Mine Legislation of Turkey: Mining Operation Activity Process </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ta%C5%9Fk%C4%B1n%20Deniz%20Y%C4%B1ld%C4%B1z">Taşkın Deniz Yıldız</a>, <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Topalo%C4%9Flu"> Mustafa Topaloğlu</a>, <a href="https://publications.waset.org/abstracts/search?q=Orhan%20Kural"> Orhan Kural</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The right to operate a fairly long periods of prior periods and after the 3213 Mining Law has been observed to be shortened in Turkey. Permit the realization of business activities (or concession) requested the purchase of the mine operated "found mine" position, as well as the financial and technical capability to have the owner of the right to operate the mines as well as the principle of equality is important in terms of assessing the best way be. In particular, in this context, license fields "negligence" (downsizing) have noted that the current arrangement for all periods. However, in the period after 3213 Mining Act and a permit to operate more effectively within the framework of implementation of negligence is laid down. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mining%20legislation" title="mining legislation">mining legislation</a>, <a href="https://publications.waset.org/abstracts/search?q=operation" title=" operation"> operation</a>, <a href="https://publications.waset.org/abstracts/search?q=permit" title=" permit"> permit</a>, <a href="https://publications.waset.org/abstracts/search?q=Turkey" title=" Turkey"> Turkey</a> </p> <a href="https://publications.waset.org/abstracts/59466/analysis-of-changes-being-done-of-the-mine-legislation-of-turkey-mining-operation-activity-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59466.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">402</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15992</span> Personalize E-Learning System Based on Clustering and Sequence Pattern Mining Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20S.%20Saini">H. S. Saini</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Vijayalakshmi"> K. Vijayalakshmi</a>, <a href="https://publications.waset.org/abstracts/search?q=Rishi%20Sayal"> Rishi Sayal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Network-based education has been growing rapidly in size and quality. Knowledge clustering becomes more important in personalized information retrieval for web-learning. A personalized-Learning service after the learners’ knowledge has been classified with clustering. Through automatic analysis of learners’ behaviors, their partition with similar data level and interests may be discovered so as to produce learners with contents that best match educational needs for collaborative learning. We present a specific mining tool and a recommender engine that we have integrated in the online learning in order to help the teacher to carry out the whole e-learning process. We propose to use sequential pattern mining algorithms to discover the most used path by the students and from this information can recommend links to the new students automatically meanwhile they browse in the course. We have Developed a specific author tool in order to help the teacher to apply all the data mining process. We tend to report on many experiments with real knowledge so as to indicate the quality of using both clustering and sequential pattern mining algorithms together for discovering personalized e-learning systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=e-learning" title="e-learning">e-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=cluster" title=" cluster"> cluster</a>, <a href="https://publications.waset.org/abstracts/search?q=personalization" title=" personalization"> personalization</a>, <a href="https://publications.waset.org/abstracts/search?q=sequence" title=" sequence"> sequence</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern" title=" pattern"> pattern</a> </p> <a href="https://publications.waset.org/abstracts/33440/personalize-e-learning-system-based-on-clustering-and-sequence-pattern-mining-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33440.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">428</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15991</span> Frequent Item Set Mining for Big Data Using MapReduce Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tamanna%20Jethava">Tamanna Jethava</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahul%20Joshi"> Rahul Joshi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Frequent Item sets play an essential role in many data Mining tasks that try to find interesting patterns from the database. Typically it refers to a set of items that frequently appear together in transaction dataset. There are several mining algorithm being used for frequent item set mining, yet most do not scale to the type of data we presented with today, so called “BIG DATA”. Big Data is a collection of large data sets. Our approach is to work on the frequent item set mining over the large dataset with scalable and speedy way. Big Data basically works with Map Reduce along with HDFS is used to find out frequent item sets from Big Data on large cluster. This paper focuses on using pre-processing & mining algorithm as hybrid approach for big data over Hadoop platform. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequent%20item%20set%20mining" title="frequent item set mining">frequent item set mining</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadoop" title=" Hadoop"> Hadoop</a>, <a href="https://publications.waset.org/abstracts/search?q=MapReduce" title=" MapReduce"> MapReduce</a> </p> <a href="https://publications.waset.org/abstracts/49592/frequent-item-set-mining-for-big-data-using-mapreduce-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49592.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge 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