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Using Subsequence Mining to Identify Business Processes in Data Networks

Using Subsequence Mining to Identify Business Processes in Data Networks
Author: Felix Kuhr
Publisher: GRIN Verlag
Total Pages: 69
Release: 2017-01-13
Genre: Computers
ISBN: 3668379645
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Master's Thesis from the year 2016 in the subject Computer Science - Commercial Information Technology, grade: -, Hamburg University of Technology (TUHH; Universität zu Lübeck), language: English, abstract: To manage business processes, companies must previously define, configure, implement and enact them. Analysts try to identify companies’ business processes. However, large companies might have complex business processs (BPs) and consist of many business units. Therefore, classical business process modelling hardly scales. Both, companies and analysts are interested in automated approaches for business process modelling, saving time and money. Today’s business process analysts often use process mining techniques to extract company’s business processes by analyzing event logs of applications. This technique has its limitations, and is strongly dependent on the kind of log files of deployed applications. By designing our mission oriented network analysis (MONA) approach using algorithms having polynomial complexity, we show that identification of business processes is tractable. Identification of related tasks which constitute business processes is based on analysis of communication patterns in network traffic. We assume that today’s business processes are based on network-aided applications. Our software presents identified business processes using business process modelling notation.

Mining Sequential Patterns from Large Data Sets

Mining Sequential Patterns from Large Data Sets
Author: Wei Wang
Publisher: Springer Science & Business Media
Total Pages: 174
Release: 2005-07-26
Genre: Computers
ISBN: 0387242473
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In many applications, e.g., bioinformatics, web access traces, system u- lization logs, etc., the data is naturally in the form of sequences. It has been of great interests to analyze the sequential data to find their inherent char- teristics. The sequential pattern is one of the most widely studied models to capture such characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. In this book, we focus on sequential pattern mining. To meet different needs of various applications, several models of sequential patterns have been proposed. We do not only study the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. The objective of this book is to provide computer scientists and domain - perts such as life scientists with a set of tools in analyzing and understanding the nature of various sequences by : (1) identifying the specific model(s) of - quential patterns that are most suitable, and (2) providing an efficient algorithm for mining these patterns. Chapter 1 INTRODUCTION Data Mining is the process of extracting implicit knowledge and discovery of interesting characteristics and patterns that are not explicitly represented in the databases. The techniques can play an important role in understanding data and in capturing intrinsic relationships among data instances. Data mining has been an active research area in the past decade and has been proved to be very useful.

Sequence Data Mining

Sequence Data Mining
Author: Guozhu Dong
Publisher: Springer Science & Business Media
Total Pages: 160
Release: 2007-10-31
Genre: Computers
ISBN: 0387699376
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Understanding sequence data, and the ability to utilize this hidden knowledge, will create a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. This book provides thorough coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. It offers balanced coverage on data mining and sequence data analysis, allowing readers to access the state-of-the-art results in one place.

Organizational Routines

Organizational Routines
Author: Markus C. Becker
Publisher: Edward Elgar Publishing
Total Pages: 303
Release: 2009-01-01
Genre: Business & Economics
ISBN: 1848447248
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One of the major challenges facing organization studies has been for a long time to develop an operational content to the notion of routines . This book offers important advances in this direction, both conceptually and through illuminating case studies. Giovanni Dosi, Sant Anna School of Advanced Studies, Pisa, Italy This book showcases advanced empirical research that applies the concept of organizational routines to understanding organizations and how they change and evolve. The contributions gathered in the book cover qualitative, quantitative, and archival methods for empirical research applying the concept of organizational routines. Specific issues highlighted include the use of event-sequence methods in the analysis of organizational routines, the impact of standard operating procedures on recurrent behaviour patterns, and the stability, resilience, and change of organizational routines. The book thus provides an overview of different empirical methods applied to study organizational routines, and of their prerequisites, analytical power, and contribution. This comprehensive book will be of great interest to scholars and postgraduate students in the fields of organization theory, strategy, and organization behaviour. Researchers in organization, management and economic science, organizational change and evolutionary theories will also find this book invaluable.

The Data Bonanza

The Data Bonanza
Author: Malcolm Atkinson
Publisher: John Wiley & Sons
Total Pages: 423
Release: 2013-03-19
Genre: Computers
ISBN: 1118540301
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Complete guidance for mastering the tools and techniques of the digital revolution With the digital revolution opening up tremendous opportunities in many fields, there is a growing need for skilled professionals who can develop data-intensive systems and extract information and knowledge from them. This book frames for the first time a new systematic approach for tackling the challenges of data-intensive computing, providing decision makers and technical experts alike with practical tools for dealing with our exploding data collections. Emphasizing data-intensive thinking and interdisciplinary collaboration, The Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business examines the essential components of knowledge discovery, surveys many of the current research efforts worldwide, and points to new areas for innovation. Complete with a wealth of examples and DISPEL-based methods demonstrating how to gain more from data in real-world systems, the book: Outlines the concepts and rationale for implementing data-intensive computing in organizations Covers from the ground up problem-solving strategies for data analysis in a data-rich world Introduces techniques for data-intensive engineering using the Data-Intensive Systems Process Engineering Language DISPEL Features in-depth case studies in customer relations, environmental hazards, seismology, and more Showcases successful applications in areas ranging from astronomy and the humanities to transport engineering Includes sample program snippets throughout the text as well as additional materials on a companion website The Data Bonanza is a must-have guide for information strategists, data analysts, and engineers in business, research, and government, and for anyone wishing to be on the cutting edge of data mining, machine learning, databases, distributed systems, or large-scale computing.

Encyclopedia of Data Warehousing and Mining

Encyclopedia of Data Warehousing and Mining
Author: Wang, John
Publisher: IGI Global
Total Pages: 1382
Release: 2005-06-30
Genre: Computers
ISBN: 1591405599
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Data Warehousing and Mining (DWM) is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead. The Encyclopedia of Data Warehousing and Mining provides a comprehensive, critical and descriptive examination of concepts, issues, trends, and challenges in this rapidly expanding field of data warehousing and mining (DWM). This encyclopedia consists of more than 350 contributors from 32 countries, 1,800 terms and definitions, and more than 4,400 references. This authoritative publication offers in-depth coverage of evolutions, theories, methodologies, functionalities, and applications of DWM in such interdisciplinary industries as healthcare informatics, artificial intelligence, financial modeling, and applied statistics, making it a single source of knowledge and latest discoveries in the field of DWM.

Frequent Pattern Mining

Frequent Pattern Mining
Author: Charu C. Aggarwal
Publisher: Springer
Total Pages: 480
Release: 2014-08-29
Genre: Computers
ISBN: 3319078216
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This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference.

Trends and Applications in Knowledge Discovery and Data Mining

Trends and Applications in Knowledge Discovery and Data Mining
Author: Xiao-Li Li
Publisher: Springer
Total Pages: 296
Release: 2015-11-25
Genre: Computers
ISBN: 3319256602
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This book constitutes the refereed proceedings at PAKDD Workshops 2015, held in conjunction with PAKDD, the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining in Ho Chi Minh City, Vietnam, in May 2015. The 23 revised papers presented were carefully reviewed and selected from 57 submissions. The workshops affiliated with PAKDD 2015 include: Pattern Mining and Application of Big Data (BigPMA), Quality Issues, Measures of Interestingness and Evaluation of data mining models (QIMIE), Data Analytics for Evidence-based Healthcare (DAEBH), Vietnamese Language and Speech Processing (VLSP).

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Author: Jiawei Han
Publisher: Elsevier
Total Pages: 740
Release: 2011-06-09
Genre: Computers
ISBN: 0123814804
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Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Applications of Data Mining in Computer Security

Applications of Data Mining in Computer Security
Author: Daniel Barbará
Publisher: Springer Science & Business Media
Total Pages: 266
Release: 2012-12-06
Genre: Computers
ISBN: 146150953X
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Data mining is becoming a pervasive technology in activities as diverse as using historical data to predict the success of a marketing campaign, looking for patterns in financial transactions to discover illegal activities or analyzing genome sequences. From this perspective, it was just a matter of time for the discipline to reach the important area of computer security. Applications Of Data Mining In Computer Security presents a collection of research efforts on the use of data mining in computer security. Applications Of Data Mining In Computer Security concentrates heavily on the use of data mining in the area of intrusion detection. The reason for this is twofold. First, the volume of data dealing with both network and host activity is so large that it makes it an ideal candidate for using data mining techniques. Second, intrusion detection is an extremely critical activity. This book also addresses the application of data mining to computer forensics. This is a crucial area that seeks to address the needs of law enforcement in analyzing the digital evidence.