Babis Theodoulidis

Literature review of spatio-temporal database models

Recent efforts in spatial and temporal data models and database systems have attempted to achieve an appropriate kind of interaction between the two areas. This paper reviews the different types of spatio-temporal data models that have been proposed in the literature as well as new theories and concepts that have emerged. It provides an overview of previous achievements within the domain and critically evaluates the various approaches through the use of a case study and the construction of a comparison framework. This comparative review is followed by a comprehensive description of the new lines of research that emanate from the latest efforts inside the spatio-temporal research community.

Towards an infrastructure for temporal databases: report of an invitational ARPA/NSF workshop

the discussion. Specifically, an initial glossary of temporal database concepts and a. test suite of temporal queries were distributed before the workshop. Both of these document*s were amended based on the analysis and critique of the workshop. A language design committee was constituted after the workshop to develop a consensus temporal query la,nguage extension to SQL-92; this design also benefited from the discussion at the workshop. This report documents the discussions and consensus reached at the workshop. The report. reflects the conclusions rea.ched at the workshop in June, 1993 and further discussions amongst the group participants through electronic mail. In preparing this report, each group coordinator assembled ideas and prepared an initial draft, which was then reviewed by a.11 the workshop participants. The record of the deliberations of these four groups, in the following four sections, forms t.he bulk of this report. Each of these sections begins with the group’s charter and a brief snapshot of the status of the field and ends with a list of follow-on efforts. The last, section identifies the workshop pa.rticipants. The full report’ provides more discussion and many additional references t,o the literature.

Ontology management and evolution for business intelligence

The amount of heterogeneous data that is available to organizations nowadays has made information management a seriously complicated task, yet crucial since this data can be a valuable asset for business intelligence. Ontologies can act as a semantically rich knowledge base in systems that specialize in information management. The present work investigates the potential of ontologies in supporting the information lifecycle within a corporate environment for business intelligence. The paper demonstrates the use of Heraclitus II, a framework that employs ontology management and evolution in the context of information management systems. The capabilities of the framework in facilitating information management and business intelligence are evaluated through a real-life case study from the life sciences industry.

Visual data mining modeling techniques for the visualization of mining outcomes

The visual senses for humans have a unique status, offering a very broadband channel for information flow. Visual approaches to analysis and mining attempt to take advantage of our abilities to perceive pattern and structure in visual form and to make sense of, or interpret, what we see. Visual Data Mining techniques have proven to be of high value in exploratory data analysis and they also have a high potential for mining large databases. In this work, we try to investigate and expand the area of visual data mining by proposing new visual data mining techniques for the visualization of mining outcomes.

Chorochronos: a research network for spatiotemporal database systems

* Andrew Frank, Stephane Grumbach, Ralf Hartmut Güting, Christian S. Jensen, Manolis Koubarakis, Nikos Lorentzos, Yannis Manolopoulos, Enrico Nardelli, Barbara Pernici, Hans-Jörg Schek, Michel Scholl, Timos Sellis, Babis Theodoulidis and Peter Widmayer. Contact author: Timos Sellis, Dept. of Electrical and Comp. Engin., National Tech. University of Athens, Zografou 15773, Athens, Greece, timos@dbnet.ece.ntua.gr

Analyzing Customer Experience Feedback Using Text Mining A Linguistics-Based Approach

Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences challenging. At the same time, advances in technology and changes in methods for collecting explicit customer feedback are generating increasing volumes of unstructured textual data, making it difficult for managers to analyze and interpret this information. Consequently, text mining, a method enabling automatic extraction of information from textual data, is gaining in popularity. However, this method has performed below expectations in terms of depth of analysis of customer experience feedback and accuracy. In this study, we advance linguistics-based text mining modeling to inform the process of developing an improved framework. The proposed framework incorporates important elements of customer experience, service methodologies, and theories such as cocreation processes, interactions, and context. This more holistic approach for analyzing feedback facilitates a deeper analysis of customer feedback experiences, by encompassing three value creation elements: activities, resources, and context (ARC). Empirical results show that the ARC framework facilitates the development of a text mining model for analysis of customer textual feedback that enables companies to assess the impact of interactive service processes on customer experiences. The proposed text mining model shows high accuracy levels and provides flexibility through training. As such, it can evolve to account for changing contexts over time and be deployed across different (service) business domains; we term it an “open learning” model. The ability to timely assess customer experience feedback represents a prerequisite for successful cocreation processes in a service environment.

Integrating database technology, rule-based systems and temporal reasoning for effective information systems: the TEMPORA paradigm

Abstract. Recent years have witnessed a growing realization that the development of large data‐intensive, transaction‐oriented information systems is becoming increasingly more difficult as user requirements become broader and more sophisticated. Contemporary approaches have been criticized for producing systems which are difficult to maintain and which provide little assistance in organizational developments. This paper introduces the TEMPORA paradigm, which is currently under development and which advocates a closer alignment between organizational policy and information system functionality. This viewpoint impacts on a number of critical issues related to the development process of information systems most notably in the nature of conceptual models, the discipline adopted for the development, the type of support provided by CASE tools and the run‐time environment. The paper introduces the philosophy and architecture of the TEMPORA paradigm and describes the conceptual models, tools and run‐time environment which render such an approach a feasible undertaking.

Analysis of stock market manipulations using knowledge discovery techniques applied to intraday trade prices

This paper addresses challenges relating to applying data mining techniques to detect stock price manipulations and extends previous results by incorporating the analysis of intraday trade prices in addition to closing prices for the investigation of trade-based manipulations. In particular, this work extends previous results on the topic by analysing empirical evidence in normal and manipulated hourly data and the particular characteristics of intraday trades within suspicious hours. Furthermore, the analytical models described in this paper reinforce the results of previous market manipulation studies that are based on traditional statistical and econometrical methods providing an alternative portfolio of methods and techniques originating from the data mining and knowledge discovery areas. With the application of the analytical approach described in this paper, it is possible to identify new fraud manipulation pattern characteristics encoded as decision trees which can be readily employed in fraud detection systems. The paper also proposes a number of policy recommendations towards increasing the effectiveness of the operational processes executed by stock exchange fraud departments and regulatory authorities.

A rule language to capture and model business policy specifications

The TEMPORA paradigm for the development of large data intensive, transaction oriented information systems explicitly recognises the role of organisational policy within an information system, and visibly maintains this policy throughout the software development process, from requirements specifications through to an executable implementation.

PARMENIDES: Towards Business Intelligence Discovery from Web Data

The amounts of heterogeneous data that are available on the Web have made information management a seriously complicated task. In the context of the present work, PARMENIDES, a business-oriented framework for information management on the Web is introduced. Semantic information is extracted from Web data and warehoused in a way that enables its efficient retrieval and reasoning towards the support for business intelligence tasks. The capabilities of the PARMENIDES approach are demonstrated via a case study based on the life sciences domain