<abstract xmlns="http://www.w3.org/1999/xhtml">In this presentation, we discuss how a data warehouse can support situational awareness and data forensic needs for investigation of event streams violating rules. The data warehouse for event streams can contain summary tables showing rule violation on different aggregation level. We will introduce the classification of rules and the concept of a general aggregation graph for defining various classes of rules violation and their relationships. The data warehouse system containing various rule violation aggregations will allow the data forensics experts to have the ability to “drill-down” into event data across different data warehouse dimensions. The event stream real-time processing and other software modules can also use the summarizations to discover if current events bursts satisfy rules by comparing them with historic event bursts.</abstract>

Department of Mathematics and Computer Science, Fayetteville State University, Fayetteville, USA

CSIIR Group, CSE Division, Oak Ridge National Laboratory, Oak Ridge, USA

Data Warehouse for Event Streams Violating Rules

<abstract xmlns="http://www.w3.org/1999/xhtml">This paper presents another approach for determining document’s semantic orientation process. It includes a brief introduction describing the area of application of opinion mining, and some definitions useful in the field. The most commonly used methods are mentioned and some alternative ones are described. Experiment results are presented which show that kNN algorithm gives similar results to proportional algorithm.</abstract>

Performance of K-Nearest Neighbors Algorithm in Opinion Classification

<abstract xmlns="http://www.w3.org/1999/xhtml">Transactional memory is a rather novel approach to concurrency control in parallel computing, that has just recently found its way into distributed systems research. However, the research concentrates mainly on single processor solutions or cluster environment. In this paper we argue, that peer-to-peer systems would require a different design of transactional memory because of the increased failure-rate of nodes, slower network and possibility of network splits. We also present a few of our design ideas, namely increased performance and fault tolerance through the use of higher-level conflict detection and resolution via abstract data types and eventually consistency, that as we think could be important to a successful implementation of a scalable and resilient transactional memory.</abstract>

Designing a Software Transactional Memory for Peer-To-Peer Systems

<abstract xmlns="http://www.w3.org/1999/xhtml">In this paper authors present data management solutions for new fingerprint recognition based on minutes groups. They compared existing fingerprint recognition data store methods and their own solutions. Authors proposed a new algorithm based on distribution minutiae' groups using selective attention algorithms.</abstract>

Data Management for Fingerprint Recognition Algorithm Based on Characteristic Points' Group

<abstract xmlns="http://www.w3.org/1999/xhtml">Data warehouses integrate external data sources (EDSs), which very often change their data structures (schemas). In many cases, such changes cause an erroneous execution of an already deployed ETL workow. Structural changes of EDSs are frequent, therefore an automatic reparation of an ETL workow, after such changes, is of a high importance. This paper presents a framework, called E-ETL, for handling the evolution of an ETL layer. Detection of changes in EDSs causes a repa- ration of the fragment of ETL workow which interacts with the changed EDSs. The proposed framework was developed as a module external to a standard commercial or open-source ETL engine, accessing the engine by means of API. The innovation of this framework consists in: (1) the algorithms for semi-automatic reparation of an ETL workow and (2) its ability to interact with various ETL engines that provide API.</abstract>

E-ETL: Framework for Managing Evolving ETL Workflows

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Foundations of Computing and Decision Sciences

ISSN: 0867-6356 (print version)  Foundations of Computing and Decision Sciences (until 1990 Foundations of Control Engineering) is a quarterly peer-reviewed international journal published since 1975. One of its main aims is its focus on the links between Computing (understood in the sense defined in the report of the ACM Task Force on the Core of Computer Science chaired by Peter J. Denning: Computing as a Discipline, CACM, Vol. 32, No. 1, 1989) and broadly understood Decision Sciences.  Scope of topics  The journal provides a forum for exchange of ideas for researchers in the fields of computing and decision sciences. The scope of the journal covers various areas of computing and decision sciences, particularly:    approximate reasoning,  artificial intelligence,  combinatorial optimization,  computational complexity theory,  databases and data warehouses,  intelligent decision support,  knowledge engineering,  machine learning and data mining,  metaheuristics,  multiple criteria decision analysis,  networking and distributed systems,  parallel computing and concurrency,  production and project scheduling,  scheduling theory,  soft and granular computing,  software engineering.     Why submit   International editorial board composed of distinguished scholars highly representative of the field of computing and decision sciences,  Quick turnaround,  Comprehensive peer review with at least two highly competent referees assessing each manuscript,  Promotion of published papers in the vast scientific community of researchers working in the field.   Rejection Rate   ca. 60%   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process (iThenticate, https://www.ithenticate.com/ ). 