Kalinka Kaloyanova

Some Extensions to the Multidimensional Data Model

Business intelligence applications involve complex queries on very large databases. Users typically view the data as multidimensional data cubes. Computing multidimensional aggregates in large data cubes is a performance bottleneck for many OLAP applications. Calculating the answer of an aggregation query can be too expensive in terms of time and storage space. In this paper we describe some of the problems that can arise in the process of building multidimensional applications with Oracle OLAP Option. We pay a special attention to the sparsity of high dimensional data cubes. We present some extensions to the common multidimensional data model which could solve described problems. They also enable more flexible interface not only for the developer of OLAP application but for the end users too

An integrated approach for RUP, EA, SOA and BPM implementation

In the software industry there are new major technology trends - i.e. Service Oriented Architecture (SOA), Business Process Management (BPM) and Enterprise Architecture - that had already gained enough popularity and adoption to have a stance on their own in the community of practice, but that have not been successfully incorporated into the standard set of software development practices. This creates a window of opportunity for the Software Engineering (SE) researchers to evaluate the inclusion of these new concepts into the existing software development process. This paper introduces the idea of an integrated framework with the aim of supporting the inclusion of SOA, BPM and EA into the Rational Unified Process (RUP). We expect that the current work will support future researchers in finding the proper path to achieve consensus for the inclusion of the mentioned topics into a common framework.

Big data techniques, systems, applications, and platforms: Case studies from academia

Big data is a broad term with numerous dimensions, most notably: big data characteristics, techniques, software systems, application domains, computing platforms, and big data milieu (industry, government, and academia). In this paper we briefly introduce fundamental big data characteristics and then present seven case studies of big data techniques, systems, applications, and platforms, as seen from academic perspective (industry and government perspectives are not subject of this publication). While we feel that it is difficult, if at all possible, to encapsulate all of the important big data dimensions in a strict and uniform, yet comprehensible language, we believe that a set of diverse case studies - like the one that is offered in this paper - a set that spreads over the principal big data dimensions can indeed be beneficial to the broad big data community by helping experts in one realm to better understand currents trends in the other realms.

Big data mining: In-database Oracle data mining over hadoop

Big data challenges different aspects of storing, processing and managing data, as well as analyzing and using data for business purposes. Applying Data Mining methods over Big Data is another challenge because of huge data volumes, variety of information, and the dynamic of the sources. Different applications are made in this area, but their successful usage depends on understanding many specific parameters. In this paper we present several opportunities for using Data Mining techniques provided by the analytical engine of RDBMS Oracle over data stored in Hadoop Distributed File System (HDFS). Some experimental results are given and they are discussed.Big data challenges different aspects of storing, processing and managing data, as well as analyzing and using data for business purposes. Applying Data Mining methods over Big Data is another challenge because of huge data volumes, variety of information, and the dynamic of the sources. Different applications are made in this area, but their successful usage depends on understanding many specific parameters. In this paper we present several opportunities for using Data Mining techniques provided by the analytical engine of RDBMS Oracle over data stored in Hadoop Distributed File System (HDFS). Some experimental results are given and they are discussed.

Oracle and Vertica for Frequent Itemset Mining

In the last few years, organizations have become much more interested in using data to create value. Big Data, however, presents new challenges to the extraction of knowledge using traditional Data Mining methods. In this paper we focus on a concrete implementation of association rules generation. The proposed algorithm is specialized for four datasets and its performance for different support thresholds is measured. This is done for two Database Management Systems (DBMS) – a traditional row-oriented DMBS in the face of Oracle and a column-oriented DBMS represented by Vertica. The results indicate the suitability of these DBMSs as tools for association rules generation.

An Approach to the Context-oriented Use Case Analysis

The paper describes our efforts to propose a feasible solution of a significant problem in information systems development – requirements engineering, based on use cases. The adjustable use case quality model is constructed and used within the Comparative Analysis method to support the decision making during the use case analysis process. Two real-life problems related to this process are described and their solutions through the suggested approach are given.

Regular Sparsity in OLAP System

One of the primary challenges of storing multidimensional data is the degree of sparsity that is often encountered. Because the extremely sparse cubes are frequent phenomenon, OLAP engines offer different methods of increasing the performance of sparse cubes, but all of these methods do not take account of the sparsity nature and did not divide the sparsity into any types. Our experience leads us to a following division of the empty areas in the multidimensional cubes: (a) areas that are empty because of the semantics of the business (the semantics enforces lack of value) and (b) areas that are empty by a chance. To formally distinguish these types of sparsity, we introduce a new object (“regular sparsity map”) which provides business analysts with the ability to define rules and place data constraints over the multidimensional cube. In this paper we present our regular sparsity map editor and discuss how it can be used for the purpose of data errors detection and selection of relevant dimension elements.