Search results for: multidimensional process mining

Authors: Anahita Farhang Ghahfarokhi, Alessandro Berti, Wil M.P. van der Aalst

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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.

Keywords: multidimensional process mining, mMulti-perspective business processes, OLAP, process cubes, process discovery, process mining

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Authors: James Newman

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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.

Keywords: blockchain, process mining, memory optimization, protocol

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Authors: Sohail Imran, Tariq Mahmood

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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.

Keywords: process mining, healthcare, diagnostic investigation process, process flow

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Authors: Sikander Singh Cheema, Jasmeen Kaur

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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.

Keywords: data mining, KDD, genetic algorithm, descriptive mining, predictive mining

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Authors: Awatef Hicheur Cairns, Billel Gueni, Hind Hafdi, Christian Joubert, Nasser Khelifa

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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.

Keywords: educational process mining, distributed process mining, clustering, distributed platform, educational data mining, ProM

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Authors: M. V. Manoj Kumar, Likewin Thomas, Annappa

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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.

Keywords: abrupt drift, concept drift, sudden drift, control-flow perspective, detection and localization, process mining

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Authors: Kyle De Freitas, Margaret Bernard

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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.

Keywords: educational data mining, learning management system, learning analytics, EDM framework

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Authors: Seung-Lock Seo

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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.

Keywords: data mining, process data, monitoring, safety, industrial processes

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Authors: Zouari Ezzeddine, Ghraieb Ikram

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From the results of the academic literature which evokes the limitations of previous studies, this article shows the reasons for multidimensionality Prediction of financial bubbles. A new framework for modeling study predicting financial bubbles by linking a set of variable presented on several dimensions dictating its multidimensional character. It takes into account the preferences of financial actors. A multicriteria anticipation of the appearance of bubbles in international financial markets helps to fight against a possible crisis.

Keywords: classical measures, predictions, financial bubbles, multidimensional, artificial neural networks

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Authors: M. V. Manoj Kumar, Likewin Thomas, Annappa

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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.

Keywords: consensus trace, process mining, multiple trace alignment, trace logo

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Authors: Ouzayr Rabhi, Ibtissam Arrassen

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To provide a complete analysis of the organization and to help decision-making, leaders need to have relevant data; Data Warehouses (DW) are designed to meet such needs. However, designing DW is not trivial and there is no formal method to derive a multidimensional schema from heterogeneous databases. In this article, we present a Model-Driven based approach concerning the design of data warehouses. We describe a multidimensional meta-model and also specify a set of transformations starting from a Unified Modeling Language (UML) metamodel. In this approach, the UML metamodel and the multidimensional one are both considered as a platform-independent model (PIM). The first meta-model is mapped into the second one through transformation rules carried out by the Query View Transformation (QVT) language. This proposal is validated through the application of our approach to generating a multidimensional schema of a Balanced Scorecard (BSC) DW. We are interested in the BSC perspectives, which are highly linked to the vision and the strategies of an organization.

Keywords: data warehouse, meta-model, model-driven architecture, transformation, UML

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Authors: Kwan Hee Han

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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.

Keywords: data mining, event log, process mining, production scheduling

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Authors: Ramya Rachel S.

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For women in a developing country like India, poverty is an ever-clinging problem which has rooted itself without any trace of absolute abolition. Poverty is a deprivation of many imminent needs and must be measured accordingly. Therefore, it is important to study the dimensions of education, health, and standard of living to understand the true nature of the impoverished. The study focused on studying the deprivation on these aspects using the Alkire-Foster methodology to estimate the Multidimensional Poverty Index. The study has utilized the individual data of women aged 15 to 49 of the National Family Health Survey (NFHS) for the year 2015-16. Findings reveal that women in India still face extreme levels of deprivation in various dimensions. More than one-third of the total women aged 15 to 24 in India were multidimensionally poor. Dimensional breakdown of the levels of multidimensional poverty indicates that the dimension of Education is the highest contributor to poverty. Decomposition of the multidimensional poverty among various demographic sub-groups, reveals that the multidimensional poverty level increases with age. Results point out that deprivations were higher among widowed and married women, and among women who lived alone. There was also a huge rural-urban divide with respect to poverty. The basic needs of these women must be targeted and met so that they are withdrawn from all forms of poverty.

Keywords: deprivations, multidimensional poverty, sub-group decomposition, women

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Authors: Maram Saleh

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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.

Keywords: Data Mining, Knowledge management, Knowledge discovery, Knowledge creation.

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Authors: Adarsh Shroff

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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.

Keywords: big data, map reduce, incremental processing, iterative computation

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Authors: Bidyadhar Dehury, Sanjay Kumar Mohanty

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According to the Right to Education Act of India, education is the fundamental right of all children of age group 6-14 year irrespective of their status. Using the unit level data from India Human Development Survey (IHDS), we tried to understand the inter-relationship between the level of poverty and the academic performance of the children aged 8-11 years. The level of multidimensional poverty is measured using five dimensions and 10 indicators using Alkire-Foster approach. The weighted deprivation score was obtained by giving equal weight to each dimension and indicators within the dimension. The weighted deprivation score varies from 0 to 1 and grouped into four categories as non-poor, vulnerable, multidimensional poor and sever multidimensional poor. The academic performance index was measured using three variables reading skills, math skills and writing skills using PCA. The bivariate and multivariate analysis was used in the analysis. The outcome variable was ordinal. So the predicted probabilities were calculated using the ordinal logistic regression. The predicted probabilities of good academic performance index was 0.202 if the child was sever multidimensional poor, 0.235 if the child was multidimensional poor, 0.264 if the child was vulnerable, and 0.316 if the child was non-poor. Hence, if the level of poverty among the children decreases from sever multidimensional poor to non-poor, the probability of good academic performance increases.

Keywords: multidimensional poverty, academic performance index, reading skills, math skills, writing skills, India

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Authors: Joseph Siani

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Over the last decades, dissatisfaction with global indicators of economic performance, such as GDP (Gross Domestic Product) per capita, has shifted the attention to what is now referred to as multidimensional poverty. In this framework, poverty goes beyond income to incorporate aspects of well-being not captured by income measures alone. This paper applies the totally fuzzy approach to estimate the fuzzy index of poverty (FIP) in fourteen Sub-Saharan African (SSA) countries using Demographic and Health Survey (DHS) data and explores whether pictures created by the standard headcount ratio at $1.90 a day and the fuzzy index of poverty tell a similar story. The results suggest that there is indeed considerable mismatch between poverty headcount and the fuzzy index of multidimensional poverty, meaning that the majority of the most deprived people (as identified by the fuzzy index of multidimensional poverty) would not be identified by the poverty headcount ratio. Moreover, we find that poverty is distributed differently by colonial heritage (language). In particular, the most deprived countries in SSA are French-speaking.

Keywords: fuzzy set approach, multidimensional poverty, poverty headcount, overlap, Sub-Saharan Africa

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Authors: Yusra Abdulsalam Alqamati, Ahmed Alkilany

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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.

Keywords: process mining, BPM, business process model and notation, Petri net, teaching staff, Google Cloud Platform

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Authors: Wanhyun Cho, Soonja Kang, Sanggoon Kim, Soonyoung Park

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We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered an efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods.

Keywords: multinomial dirichlet classification model, Gaussian process priors, variational Bayesian approximation, importance sampling, approximate posterior distribution, marginal likelihood evidence

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Authors: Hoda A. Abdel Hafez

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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.

Keywords: mining big data, big data, machine learning, telecommunication

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Authors: Ketan Mahawer, Abeer Mutto, S. K. Gupta

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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.

Keywords: forward osmosis, nanofiltration, mining, draw solution, divalent solute

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Authors: Eugenia Jilinguirian

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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.

Keywords: data mining, healthcare, big data, individualised healthcare, healthcare solutions, database

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Authors: Rokhmatul Insani, Hira Laksmiwati Soemitro

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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.

Keywords: business intelligence, customer segmentation, data warehouse, data mining

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Authors: Usman Qamar, Younus Javed

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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%.

Keywords: rough-sets, classification, feature selection, entropy, outliers, frequent itemset mining

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Authors: Charles Amoatey

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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.

Keywords: risk, assessment, mining, Ghana

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Authors: Anurag Savarnya

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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.

Keywords: reliability, Weibull model, electric mining shovel

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Authors: Tosin Ige

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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.

Keywords: data, privacy, data mining, association rule, privacy preserving, mining technique

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Authors: G. Surendra Babu

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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.

Keywords: reclamation, open-pit mining, revegetation, reclamation methods

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Authors: B. Schmid, C. Kexel, E. Djafarova

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Understanding consumers is elementary for practitioners in marketing. Consumers of sports events, the sports spectators, are a particularly complex consumer crowd. In order to identify and define their profiles different segmentation approaches can be found in literature, one of them being multidimensional segmentation. Multidimensional segmentation models correspond to the broad range of attitudes, behaviours, motivations and beliefs of sports spectators, other than earlier models. Moreover, in sports there are some well-researched disciplines (e.g. football or North American sports) where consumer profiles and marketing strategies are elaborate and others where no research at all can be found. For example, there is almost no research on athletics spectators. This paper explores the current state of research on sports spectators segmentation. An in-depth literature review provides the framework for a spectators segmentation in athletics. On this basis, additional potential consumer groups and implications for social media marketing will be explored. The findings are the basis for further research.

Keywords: multidimensional segmentation, social media, sports marketing, sports spectators segmentation

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Authors: Adewale O. Ogunde, Olusegun Folorunso, Adesina S. Sodiya

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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.

Keywords: adaptivity, data mining, distributed association rule mining, incremental mining, mobile agents

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