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Contrast Data Mining
A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life ProblemsContrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and
Database and Expert Systems Applications
th 2002 DEXA, the 13 International Conference on Database and Expert Systems Applications was held on September 2–6, 2002, at the Université Aix–Marseille II, France. The quickly growing field of information systems required the establishment of more specialized discussion platforms (the DaWaK conference, EC-Web conference, eGOV conference and DEXA workshops), and there were held in parallel with DEXA, also in Aix-en-Provence. The resulting book was prepared with great effort. Starting with the preparation of submitted papers, the papers went through the reviewing process. The accepted papers were revised to final versions by their authors and arranged to the conference program. This ye...
Conceptual Structures for Knowledge Creation and Communication
The book constitutes the refereed proceedings of the 11th International Conference on Conceptual Structures, ICCS 2003, held in Dresden, Germany in July 2003. The 23 revised full papers presented together with 5 invited papers were carefully reviewed and selected for presentation. The papers are organized in topical sections on the many facets of conceptual structures, logical and linguistic aspects, conceptual representation of time and space, deepening the formal theory and applications of conceptual structures.
New Trends in Data Warehousing and Data Analysis
Most of modern enterprises, institutions, and organizations rely on knowledge-based management systems. In these systems, knowledge is gained from data analysis. Today, knowledge-based management systems include data warehouses as their core components. Data integrated in a data warehouse are analyzed by the so-called On-Line Analytical Processing (OLAP) applications designed to discover trends, patterns of behavior, and anomalies as well as finding dependencies between data. Massive amounts of integrated data and the complexity of integrated data coming from many different sources make data integration and processing challenging. New Trends in Data Warehousing and Data Analysis brings together the most recent research and practical achievements in the DW and OLAP technologies. It provides an up-to-date bibliography of published works and the resource of research achievements. Finally, the book assists in the dissemination of knowledge in the field of advanced DW and OLAP.
Formal Concept Analysis
This book constitutes the refereed proceedings of the 8th International Conference on Formal Concept Analysis, ICFCA 2010, held in Agadir, Morocco, in March 2010. The 17 revised full papers presented together with 4 invited lectures and a reprint of a historical paper were carefully reviewed and selected from 37 submissions. The papers comprise state of the art research and present new results in applied lattice and order theory, and in particular advances in theory and applications of Formal Concept Analysis. These results range from theoretical novelties to advances in FCA-related algorithmic issues, as well as application domains of FCA such as data visualization, information retrieval, machine learning, data analysis and knowledge management.
Data Warehousing and Knowledge Discovery
Data Warehousing and Knowledge Discovery have been widely accepted as key te- nologies for enterprises and organizations as a means of improving their abilities in data analysis, decision support, and the automatic extraction of knowledge from data. With the exponentially growing amount of information to be included in the decision making process, the data to be processed is becoming more and more complex in both structure and semantics. Consequently, the process of retrieval and knowledge disc- ery from this huge amount of heterogeneous complex data constitutes the reality check for research in the area. During the past few years, the International Conference on Data Warehousing and Knowled...
Data Warehousing and Knowledge Discovery
For more than a decade, data warehousing and knowledge discovery technologies have been developing into key technologies for decision-making processes in com- nies. Since 1999, due to the relevant role of these technologies in academia and ind- try, the Data Warehousing and Knowledge Discovery (DaWaK) conference series have become an international forum where both practitioners and researchers share their findings, publish their relevant results and dispute in depth research issues and experiences on data warehousing and knowledge discovery systems and applications. The 7th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2005) continued series of successful confer...
Utilizing Big Data Paradigms for Business Intelligence
Because efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations, data analysis is an important part of modern business administration. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Utilizing Big Data Paradigms for Business Intelligence is a pivotal reference source that provides vital research on how to address the challenges of data extraction in business intelligence using the five “Vs” of big data: velocity, volume, value, variety, and veracity. This book is ideally designed for business analysts, investors, corporate managers, entrepreneurs, and researchers in the fields of computer science, data science, and business intelligence.
Foundations of Data Quality Management
Data quality is one of the most important problems in data management. A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the...
Advances in Database Technology - EDBT 2000
EDBT 2000 is the seventh conference in a series dedicated to the advancement of database technology. This year’s conference special theme, \Connect Millions of Users and Data Sources," underscores the importance of databases for the information age that is dawning with the new millennium. The importance - rives not just from the observation that the information age essentially rests on theconvergenceofcommunications,computing,andstorage.Equallyimportant, many of the concepts and techniques underlying the success of databasesystems have independent meaning and impact for today’s distributed information s- tems. The papers in the volume should also be seen in this light. The EDBT 2000 conf...
