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Author: Et Al Publisher: Grin Publishing ISBN: 9783668443112 Category : Languages : en Pages : 20
Book Description
Research Paper (postgraduate) from the year 2008 in the subject Computer Science - Applied, grade: 4.0, University of Louisville (Speed College of Engineering), language: English, abstract: We give an overview of our experience in utilizing several open source packages and composing them into sophisticated applications to solve several challenging problems as part of some of the research projects at the Knowledge Discovery & Web Mining lab at the Universe of Louisville. The projects have a common theme of knowledge discovery, however their application domains span a variety of areas. These areas range from mining Web data streams to mining Astronomy related image data, as well as Web information retrieval in social multimedia websites and e-learning platforms. As is already known, a significant proportion of the effort in any real life project involving knowledge discovery in data (KDD) is devoted to the early and final stages of KDD, i.e., the data collection and preprocessing, and the visualization of the results. Given the nature of the data in our projects, we expose our experience in handling text data and image data as part of the KDD process. In addition to the open source packages that we used, we will briefly present some of the stand-alone software that we developed in the lab, in particular a suite of software for clustering and for stream data mining.
Author: Et Al Publisher: Grin Publishing ISBN: 9783668443112 Category : Languages : en Pages : 20
Book Description
Research Paper (postgraduate) from the year 2008 in the subject Computer Science - Applied, grade: 4.0, University of Louisville (Speed College of Engineering), language: English, abstract: We give an overview of our experience in utilizing several open source packages and composing them into sophisticated applications to solve several challenging problems as part of some of the research projects at the Knowledge Discovery & Web Mining lab at the Universe of Louisville. The projects have a common theme of knowledge discovery, however their application domains span a variety of areas. These areas range from mining Web data streams to mining Astronomy related image data, as well as Web information retrieval in social multimedia websites and e-learning platforms. As is already known, a significant proportion of the effort in any real life project involving knowledge discovery in data (KDD) is devoted to the early and final stages of KDD, i.e., the data collection and preprocessing, and the visualization of the results. Given the nature of the data in our projects, we expose our experience in handling text data and image data as part of the KDD process. In addition to the open source packages that we used, we will briefly present some of the stand-alone software that we developed in the lab, in particular a suite of software for clustering and for stream data mining.
Author: Ladjel Bellatreche Publisher: Springer ISBN: 3642401317 Category : Computers Languages : en Pages : 387
Book Description
This book constitutes the refereed proceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2013 held in Prague, Czech Republic, in August 2013. The 24 revised full papers and 8 short papers presented were carefully reviewed and selected from 89 submissions. The papers are organized in topical sections on modeling and ETL, query optimization and parallelism, spatial data warehouses and applications, text mining and OLAP, recommendation and prediction, data mining optimization and machine learning techniques, mining and processing data streams, clustering and data mining applications, social network and graph mining, and event sequence and Web mining.
Author: Domenico Talia Publisher: CRC Press ISBN: 1439875316 Category : Computers Languages : en Pages : 233
Book Description
A new approach to distributed large-scale data mining, service-oriented knowledge discovery extracts useful knowledge from today’s often unmanageable volumes of data by exploiting data mining and machine learning distributed models and techniques in service-oriented infrastructures. Service-Oriented Distributed Knowledge Discovery presents techniques, algorithms, and systems based on the service-oriented paradigm. Through detailed descriptions of real software systems, it shows how the techniques, models, and architectures can be implemented. The book covers key areas in data mining and service-oriented computing. It presents the concepts and principles of distributed knowledge discovery and service-oriented data mining. The authors illustrate how to design services for data analytics, describe real systems for implementing distributed knowledge discovery applications, and explore mobile data mining models. They also discuss the future role of service-oriented knowledge discovery in ubiquitous discovery processes and large-scale data analytics. Highlighting the latest achievements in the field, the book gives many examples of the state of the art in service-oriented knowledge discovery. Both novices and more seasoned researchers will learn useful concepts related to distributed data mining and service-oriented data analysis. Developers will also gain insight on how to successfully use service-oriented knowledge discovery in databases (KDD) frameworks.
Author: Christian Böhm Publisher: Springer Science & Business Media ISBN: 3642237398 Category : Computers Languages : en Pages : 218
Book Description
The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between Grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This special issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems highlights some of the major challenges emerging from the biomedical applications that are currently inspiring and promoting database research. These include the management, organization, and integration of massive amounts of heterogeneous data; the semantic gap between high-level research questions and low-level data; and privacy and efficiency. The contributions cover a large variety of biological and medical applications, including genome-wide association studies, epidemic research, and neuroscience.
Author: Francesco Bonchi Publisher: Springer ISBN: 3540332936 Category : Computers Languages : en Pages : 259
Book Description
This book presents the thoroughly refereed joint postproceedings of the 4th International Workshop on Knowledge Discovery in Inductive Databases, October 2005. 20 revised full papers presented together with 2 are reproduced here. Bringing together the fields of databases, machine learning, and data mining, the papers address various current topics in knowledge discovery and data mining in the framework of inductive databases such as constraint-based mining, database technology and inductive querying.
Author: A Min Tjoa Publisher: Springer ISBN: 3540317325 Category : Computers Languages : en Pages : 551
Book Description
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 conferences dedicated to these topics. In this edition, the conference tried to provide the right, logical balance between data warehousing and knowledge discovery. Regarding data warehousing, papers cover different relevant and still unsolved research problems, such as the modelling of ETL processes and integration problems, designing OLAP technologies from XML do- ments, modelling data warehouses and data mining applications together, impro- ments in query processing, partitioning and implementations. With regard to data mining, a variety of papers were presented on subjects including data mining te- niques, clustering, classification, text documents and classification, and patterns. These proceedings contain the technical papers that were selected for presentation at the conference. We received 196 abstracts, and finally received 162 papers from 38 countries, and the Program Committee eventually selected 51 papers, making an acceptance rate of 31.4 % of submitted papers.
Author: Mohammad Essaaidi Publisher: Springer ISBN: 3642152112 Category : Technology & Engineering Languages : en Pages : 316
Book Description
The 33 peer-reviewed contributions published in this book address a wide range of topics related to the theory and applications of intelligent distributed computing and multi-agent systems. They cover topics from bio-informatics to semantic web services.
Author: Shichao Zhang Publisher: Springer Science & Business Media ISBN: 9781852337032 Category : Computers Languages : en Pages : 250
Book Description
The Web has emerged as a large, distributed data repository, and information on the Internet and in existing transaction databases can be analyzed for commercial gains in decision making. Therefore, how to efficiently identify quality knowledge from different data sources uncovers a significant challenge. This challenge has attracted wide interest from both academia and the industry. Knowledge Discovery in Multiple Databases provides a comprehensive introduction to the latest advancements in multi-database mining, and presents a local-pattern analysis framework for pattern discovery from multiple data sources. Based on this framework, data preparation techniques in multiple databases, an application-independent database classification for data reduction, and efficient algorithms for pattern discovery from multiple databases are described. Knowledge Discovery in Multiple Databases is suitable for researchers, professionals and students in data mining, distributed data analysis, and machine learning, who are interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might involve knowledge discovery in databases and data mining.
Author: Radek Silhavy Publisher: Springer ISBN: 3319336258 Category : Technology & Engineering Languages : en Pages : 523
Book Description
This volume is based on the research papers presented in the 5th Computer Science On-line Conference. The volume Artificial Intelligence Perspectives in Intelligent Systems presents modern trends and methods to real-world problems, and in particular, exploratory research that describes novel approaches in the field of artificial intelligence. New algorithms in a variety of fields are also presented. The Computer Science On-line Conference (CSOC 2016) is intended to provide an international forum for discussions on the latest research results in all areas related to Computer Science. The addressed topics are the theoretical aspects and applications of Computer Science, Artificial Intelligences, Cybernetics, Automation Control Theory and Software Engineering.
Author: Kweku-Muata Osei-Bryson Publisher: CRC Press ISBN: 1482212382 Category : Business & Economics Languages : en Pages : 404
Book Description
Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to id
Author: Et Al
Author: Et Al
Author: Ladjel Bellatreche
Author: Domenico Talia
Author: Christian Böhm
Author: Francesco Bonchi
Author: A Min Tjoa
Author: Mohammad Essaaidi
Author: Shichao Zhang
Author: Radek Silhavy
Author: Kweku-Muata Osei-Bryson