Data mining Process of extracting and discovering patterns in large data sets Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use.[1][2][3][4] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[5] Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Etymology[edit] Background[edit] The manual extraction of patterns from data has occurred for centuries. Process[edit]
Information Retrieval Systems Information Studies 277 -- Information Retrieval Systems: User-Centered Designs Phil Agre Office: 229 GSE&IS Building Phone: (310) 825-7154 Email: pagre@ucla.edu Home: Fall 2007 Wednesdays from 9am to 12:30pm, GSE&IS room 245 This is a course on the semantic web, an important new document-centered computing technology in which web pages and other online resources are provided with metadata that can be automatically processed by computers. The course prerequisites are IS 245 (Information Access) and IS 260 (Information Structures). The course complements several other courses in the program, including IS 240 (Management of Digital Records), IS 270 (Introduction to Information Technology), IS 272 (Human/Computer Interaction), IS 274 (Database Management Systems), IS 276 (Information Retrieval Systems: Structures and Algorithms), and IS 464 (Metadata). The main idea of the semantic web is machine-readable ontology standards. Week 1. Week 2. Week 3.
Elastic times Today was a good day, so I thought I would share its results immediately, instead of fine-tuning forever — who knows when I find the time anyways! I built a little facet browser for the New York Times Article Search API - an impressively fast faceted search engine covering over two million articles. So, give it a spin! Some caveats: Don’t look for the page navigation — there is none. Pure laziness, will update it soon. The code is based on my totally revamped elastic lists prototype. Data warehouse Data Warehouse Overview In computing, a data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a database used for reporting and data analysis. Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW). The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). A data warehouse constructed from integrated data source systems does not require ETL, staging databases, or operational data store databases. A data mart is a small data warehouse focused on a specific area of interest. This definition of the data warehouse focuses on data storage. Benefits of a data warehouse[edit] A data warehouse maintains a copy of information from the source transaction systems. Generic data warehouse environment[edit] The environment for data warehouses and marts includes the following: Metadata are data about data. History[edit] Information storage[edit]
A Case for Interaction: A Study of Interactive Information Retrieval Behavior and Effectiveness Jürgen Koenemann Center for Cognitive Science Rutgers University Psychology Bldg. Frelinghuysen Rd. Piscataway, NJ 08855 USA +1 908 445 6122 koeneman@ruccs.rutgers.edu Nicholas J. This study investigates the use and effectiveness of an advanced information retrieval (IR) system (INQUERY). 64 novice IR system users were studied in their use of a baseline version of INQUERY compared with one of three experimental versions, each offering a different level of interaction with a relevance feedback facility for automatic query reformulation. Keywords: information retrieval, user interfaces, evaluation, empirical studies, relevance feedback We are experiencing in our work and home environments a dramatic explosion of information sources that become available to an exponentially growing number of users. This situation has stimulated increasing interest in computerized tools that support end-users in their information seeking tasks. Subjects Materials Experimental Design and Procedure Training
Knowledge extraction Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL (data warehouse), the main criteria is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. Overview[edit] After the standardization of knowledge representation languages such as RDF and OWL, much research has been conducted in the area, especially regarding transforming relational databases into RDF, identity resolution, knowledge discovery and ontology learning. The following criteria can be used to categorize approaches in this topic (some of them only account for extraction from relational databases):[2] Examples[edit] XML[edit]
A book by C. J. van RIJSBERGEN B.Sc., Dip. Information Retrieval Group, University of Glasgow PREFACE TO THE SECOND EDITION (London: Butterworths, 1979) The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. PREFACE TO THE FIRST EDITION (London: Butterworths, 1975) The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. I had to face the problem of balancing clarity of exposition with density of references. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. I should like to acknowledge my considerable debt to many people and institutions that have helped me. C.J.v.R. Preface 1. 2. 3. 4. 5. 6. 7. 8. Bibliography The book is also available in Adobe Acrobat format Preface 1. 2. 3. 4. 5. 6. 7. 8. First Chapter: Introduction
Knowledge retrieval Knowledge Retrieval seeks to return information in a structured form, consistent with human cognitive processes as opposed to simple lists of data items. It draws on a range of fields including epistemology (theory of knowledge), cognitive psychology, cognitive neuroscience, logic and inference, machine learning and knowledge discovery, linguistics, and information technology. Overview[edit] In the field of retrieval systems, established approaches include: Data Retrieval Systems (DRS), such as database management systems, are well suitable for the storage and retrieval of structured data.Information Retrieval Systems (IRS), such as web search engines, are very effective in finding the relevant documents or web pages. Both approaches require a user to read and analyze often long lists of data sets or documents in order to extract meaning. The goal of knowledge retrieval systems is to reduce the burden of those processes by improved search and representation. References[edit]
TREC Video Retrieval Evaluation Home Page The TREC conference series is sponsored by the National Institute of Standards and Technology (NIST) with additional support from other U.S. government agencies. The goal of the conference series is to encourage research in information retrieval by providing a large test collection, uniform scoring procedures, and a forum for organizations interested in comparing their results. In 2001 and 2002 the TREC series sponsored a video "track" devoted to research in automatic segmentation, indexing, and content-based retrieval of digital video. Beginning in 2003, this track became an independent evaluation (TRECVID) with a workshop taking place just before TREC. Paul Over is the TRECVID Project Leader at NIST. Alan Smeaton (Insight Centre for Data Analytics, Dublin City University) and Wessel Kraaij (TNO and the Institute for Computing and Information Sciences, Radboud University Nijmegen) serve as general external coordinators. The application period for TRECVID 2014 is now closed.