Natalija Kozmina

OLAP Personalization with User-Describing Profiles

In this paper we have highlighted five existing approaches for introducing personalization in OLAP: preference constructors, dynamic personalization, visual OLAP, recommendations with user session analysis and recommendations with user profile analysis and have analyzed research papers within these directions. We have pointed out applicability of personalization to OLAP schema elements in these approaches. The comparative analysis has been made in order to highlight a certain personalization approach. A new method has been proposed, which provides exhaustive description of interaction between user and data warehouse, using the concept of Zachman Framework [1, 2], according to which a set of user-describing profiles (user, preference, temporal, spatial, preferential and recommendational) have been developed. Methods of profile data gathering and processing are described in this paper.

Performance Measurement Framework with Formal Indicator Definitions

Definition of appropriate measures of organization’s performance should be conducted in a systematic way. In this paper the performance measurement and indicators are discussed not only from the side of management models, but also from the point of view of measurement theories to find out appropriate definitions. In our work we propose a formal specification of indicators. The principles of indicator reformulation from free form indicators to formal requirements are formulated and applied in several examples from performance measures database. The formally defined indicators could be used in the proposed performance measurement framework that covers five-step indicator lifecycle.

Research Directions of OLAP Personalizaton

In this paper we have highlighted five existing approaches for introducing personalization in OLAP: preference constructors, dynamic personalization, visual OLAP, recommendations with user session analysis and recommendations with user profile analysis and have analyzed research papers within these directions. We have provided an evaluation in order to point out (i) personalization options, described in these approaches, and its applicability to OLAP schema elements, aggregate functions, OLAP operations, (ii) the type of constraints (hard, soft or other), used in each approach, (iii) the methods for obtaining user preferences and collecting user information. The goal of our paper is to systematize the ideas proposed already in the field of OLAP personalization to find out further possibility for extending or developing new features of OLAP personalization.

On Implicitly Discovered OLAP Schema-Specific Preferences in Reporting Tool

On Implicitly Discovered OLAP Schema-Specific Preferences in Reporting Tool We propose content-based methods for construction of recommendations for reports in the OLAP reporting tool. Recommendations are generated based on preference information in user profile, which is updated implicitly by collecting and analyzing user activity in the reporting tool. Taking advantage of data about user preferences for data warehouse schema elements, existing reports that potentially may be interesting to the user are distinguished and recommended. The approach used for recommending reports is composed of two methods - cold-start and hot-start.

Handling Evolving Data Warehouse Requirements

A data warehouse is a dynamic environment and its business requirements tend to evolve over time, therefore, it is necessary not only to handle changes in data warehouse data, but also to adjust a data warehouse schema in accordance with changes in requirements. In this paper, we propose an approach to propagate modified data warehouse requirements in data warehouse schemata. The approach supports versions of data warehouse schemata and employs the requirements formalization metamodel and multiversion data warehouse metamodel to identify necessary changes in a data warehouse.

Towards Introducing User Preferences in OLAP Reporting Tool

This paper presents an OLAP reporting tool and an approach for determining and processing user OLAP preferences, which are useful for generating recommendations on potentially interesting reports. We discuss the metadata layers of the reporting tool including our proposed OLAP preferences metamodel, which supports various scenarios of formulating preferences of two different types: schema-specific and report-specific. The process of semantic metadata usage at the stage of formulating user preferences is also considered. The methods for processing schema-specific and report-specific OLAP preferences are outlined.

Information Requirements for Big Data Projects: A Review of State-of-the-Art Approaches.

Big data technologies are rapidly gaining popularity and become widely used, thus, making the choice of developing methodologies including the approaches for requirements analysis more acute. There is a position that in the context of the Data Warehousing (DW), similar to other Decision Support Systems (DSS) technologies, defining information requirements (IR) can increase the chances of the project to be successful with its goals achieved. This way, it is important to examine this subject in the context of Big data due to the lack of research in the field of Big data requirements analysis. This paper gives an overview of the existing methods associated with Big data technologies and requirements analysis, and provides an evaluation by three types of criteria: (i) general characteristics, (ii) requirements analysis related, and (iii) Big data technologies related criteria. We summarize on the requirements analysis process in Big data projects, and explore solutions on how to (semi-) automate requirements engineering phases.

Gathering Formalized Information Requirements of a Data Warehouse.

Information requirements of a data warehouse (DW) captured in natural language often have a common issue of being ambiguous, inaccurate, or repeating. We offer an approach to formalize DW information requirements based on our experience of using demand-driven methodology for DW conceptual design and distinction between quantifying and qualifying data. In this paper we demonstrate a working prototype of the iReq tool implemented for the purpose of collecting DW information requirements. Graphical user interface (GUI) of the iReq tool conforms to the requirement formalization metamodel acquired as a result of our previous research studies, is intuitive and user-friendly, and allows to define an unlimited number of requirement counterpart elements. The functionality of the iReq tool is wide; it allows deriving a conceptual model of a DW in a semi-automatic manner from gathered information requirements. Due to space limitations, in this paper we cover only such components as GUI for input of the information requirements illustrated with application examples, its underlying formal requirement repository, and a graph database (DB) to represent a glossary of terms for requirement definition.

Data Modelling for Dynamic Monitoring of Vital Signs: Challenges and Perspectives

The use-case described in this paper covers data acquisition and real-time analysis of the gathered medical data from wearable sensor system. Accumulated data is essential for monitoring vital signs and tracking the dynamics of the treatment process of disabled patients or patients undergoing the recovery after traumatic knee joint injury (e.g. post-operative rehabilitation). The main goal of employing the wearable sensor system is to conduct rehabilitation process more effectively and increase the rate of successful rehabilitation. The results of data analysis of patient’s vital signs and feedback allow a physiotherapist to adjust the rehabilitation scenario on the fly. In this paper, we focus on the methodology for data modelling with a purpose to design a computer-aided rehabilitation system that would support agility of changing information requirements by being flexible and augmentable.

New Trends in Databases and Information Systems: Contributions from ADBIS 2017 Workshops

In the last few years, research on database and information system technologies has been rapidly evolving thanks to the new paradigms of software and hardware adopted by modern scientific and more invasive applications. A huge and heterogeneous amount of data should be efficiently stored, managed, and analyzed exploiting proper technologies for such novel and more interesting data-driven applications. New and cutting-edge research challenges arise that have been attracting great attention from both academia and industry. The 21st European Conference on Advances in Databases and Information Systems (ADBIS 2017), held on September 24–27, 2017 in Nicosia, Cyprus includes four thematic workshops covering some emerging issues concerning such new trends in database and information system research. The aim of this paper is to present such events, their motivations and topics of interest, as well as briefly outline their programs including interesting keynotes, invited papers and a wide range of research, application, and industrial contributions selected for presentations. The selected papers have been included in this volume.