Text and Data Mining (TDM)

Well-defined theoretical results and empirical studies that have potential impact on the acquisition, management, storage, and graceful degeneration of knowledge and data, as well as in provision of knowledge and data services. Specific topics include, but are not limited to: a) artificial intelligence techniques, including speech, voice, graphics, images, and documents; b) knowledge and data engineering tools and techniques; c) parallel and distributed processing; d) real-time distributed; e) system architectures, integration, and modelling; f) database design, modelling and management; g) query design and implementation languages; h) distributed database control; j) algorithms for data and knowledge management; k) performance evaluation of algorithms and systems; l) data communications aspects; m) system applications and experience; n) knowledge-based and expert systems; and, o) integrity, security, and fault tolerance.

A forum for the examination of issues related to the research and applications of Artificial Intelligence techniques in data analysis across a variety of disciplines. These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing. In particular, papers are preferred that discuss development of new AI related data analysis architectures, methodologies, and techniques and their applications to various domains.

Contains papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include: scalable and effective algorithms for data mining and data warehousing, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices.

Forum for world-class research exploring the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data, including data science, big data infrastructure and analytics, and pervasive computing. Addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions.

The journal is concerned with the intersection of computing and the disciplines of the humanities, with tools provided by computing such as data visualization, information retrieval, statistics, text mining by publishing scholarly work beyond the traditional humanities.

NDIR is a quarterly sourcebook published by Jossey-Bass/Wiley. Each issue focuses on a specific topic related to IR, planning, or higher education management. Past editions have included articles on the use of large-scale data sets, using 'big data', and data-mining and its applications in Higher Education.

DSH or Digital Scholarship in the Humanities is an international, peer reviewed journal which publishes original contributions on all aspects of digital scholarship in the Humanities including, but not limited to, the field of what is currently called the Digital Humanities. Long and short papers report on theoretical, methodological, experimental, and applied research and include results of research projects, descriptions and evaluations of tools, techniques, and methodologies, and reports on work in progress. DSH also publishes reviews of books and resources.

Recent technological advances in sensors, embedded devices, and smart mobile devices and in applications, such as social networks, are making possible to pervasively capture huge amounts of data from many different contexts in both the physical world and the cyber world. Technologies, such as cloud systems, high-performance computing, data analytics, make possible to not only to store huge amounts of data—often referred to as big data, but also to process data for many different purposes.