George Karabatis

Specifying interdatabase dependencies in a multidatabase environment

The problem of interdatabase dependencies and the effect they have on applications updating interdependent data are addressed. A model that allows specifications of constraints among multiple databases in a declarative fashion is proposed. The separation of the constraints from the application programs facilitates the maintenance of data constraints and allows flexibility in their implementation. It allows investigation of various mechanisms for enforcing the constraints, independently of the application programs. By grouping the constraints together, it is possible to check their completeness and discover possible contradictions among them. The concepts of polytransactions, which use interdatabase dependencies to generate a series of related transactions that maintain mutual consistency among interrelated databases, is discussed.<<ETX>>

Discrete wavelet transform-based time series analysis and mining

Time series are recorded values of an interesting phenomenon such as stock prices, household incomes, or patient heart rates over a period of time. Time series data mining focuses on discovering interesting patterns in such data. This article introduces a wavelet-based time series data analysis to interested readers. It provides a systematic survey of various analysis techniques that use discrete wavelet transformation (DWT) in time series data mining, and outlines the benefits of this approach demonstrated by previous studies performed on diverse application domains, including image classification, multimedia retrieval, and computer network anomaly detection.

Semantic integration of government data for water quality management

Normative models of e-government typically assert that horizontal (i.e., inter-agency) and vertical (i.e., inter-governmental) integration of data flows and business processes represent the most sophisticated form of e-government, delivering the greatest payoff for both governments and users. This paper concentrates on the integration of data supporting water quality management as an example of how such integration can enable higher levels of e-government. It describes a prototype system that allows users to integrate water monitoring data across many federal, state, and local government organizations and provides novel techniques for information discovery, thus improving information quality and availability for decision making. Specifically, this paper outlines techniques to integrate numerous water quality monitoring data sources, to resolve data disparities, and to retrieve data using semantic relationships among data sources taking advantage of customized user profiles. Preliminary user feedback indicates that these techniques enhance quantity and quality of information available for water quality management.

Supporting mobile decision making with association rules and multi-layered caching

We describe a methodology and a prototype implementation of an online analytical processing system for mobile devices. The system guides the user to narrow down the search space using association rules. We also describe multi-layered caching techniques to improve performance and increase system utilization even in the presence of disconnections. The system is built using a three-tier architecture comprising of a data warehouse, a middle-tier server, and client mobile devices. Finally we conducted a series of simulation experiments to evaluate the performance of our association rule-based system and the multi-layered caching.

SEMANTIC INTEGRATION AND KNOWLEDGE DISCOVERY FOR ENVIRONMENTAL RESEARCH

Environmental research and knowledge discovery both require extensive use of data stored in various sources and created in different ways for diverse purposes. We describe a new metadata approach to elicit semantic information from environmental data and implement semantic-based techniques to assist users in integrating, navigating, and mining multiple environmental data sources. Our system contains specifications of various environmental data sources and the relationships that are formed among them. User requests are augmented with semantically related data sources and automatically presented as a visual semantic network. In addition, we present a methodology for data navigation and pattern discovery using multi-resolution browsing and data mining. The data semantics are captured and utilized in terms of their patterns and trends at multiple levels of resolution. We present the efficacy of our methodology through experimental results.

Enterprise Data Quality: A Pragmatic Approach

Enterprise data—the data that is created, used and shared by a corporation in conducting business—is a critical business resource that must be analyzed, architected and managed with data quality as a guiding principle. This paper presents results, practical insights, and lessons learned from a large scale study conducted in the telecommunications industry that synthesizes data quality issues into an architectural and management approach. We describe the real life case study and show how requirements for data quality were collected, how the data quality metrics were defined, what guidelines were established for intersystem data flows, what COTS (commercial off-the-shelf) technologies were used, and what results were obtained through a prototype effort. As a result of experience gained and lessons learned, we propose a comprehensive data quality approach that combines data quality and data architectures into a single framework with a series of steps, procedures, checklists, and tools. Our approach takes into account the technology, process, and people issues and extends the extant literature on data quality.

A Contextual Anomaly Detection Approach to Discover Zero-Day Attacks

There is a considerable interest in developing techniques to detect zero-day (unknown) cyber-attacks, and considering context is a promising approach. This paper describes a contextual misuse approach combined with an anomaly detection technique to detect zero-day cyber attacks. The contextual misuse detection utilizes similarity with attack context profiles, and the anomaly detection technique identifies new types of attacks using the One Class Nearest Neighbor (1-NN) algorithm. Experimental results on the NSL-KDD intrusion detection dataset have shown that the proposed approach is quite effective in detecting zero-day attacks.

Using Semantic Networks and Context in Search for Relevant Software Engineering Artifacts

The discovery of relevant software artifacts can increase software reuse and reduce the cost of software development and maintenance. Furthermore, change requests, which are a leading cause of project failures, can be better classified and handled when all relevant artifacts are available to the decision makers. However, traditional full-text and similarity search techniques often fail to provide the full set of relevant documents because they do not take into consideration existing relationships between software artifacts. We propose a metadata approach with semantic networks which convey such relationships. Our approach reveals additional relevant artifacts that the user might have not been aware of. We also apply contextual information to filter out results unrelated to the user contexts, thus, improving the precision of the search results. Experimental results show that the combination of semantic networks and context significantly improve the precision and recall of the search results.

Personalization and visualization on handheld devices

The small screen size of handheld mobile devices poses an inherent problem in visualizing data: very often it is too difficult and unpleasant to navigate through the plethora of presented information. This paper presents a novel approach to personalized and adaptive content presentation for handheld devices, which has been implemented in a mobile financial application system based on a 3-tier architecture. The approach is independent of wireless networks and mobile devices. It utilizes a combination of user profiling, data clustering, and visualization techniques (fisheye and semantic zooming), enhancing the understandability of the data and improving the usability of the device.

Enforcing data dependencies in cooperative information systems

Interdependent data are data objects in a cooperative information environment that are related by mutual consistency requirements. A flexible framework for specifying the dependency requirements of interdependent data using data dependency descriptors is discussed. A mechanism called polytransactions is presented to automatically generate actions to restore the consistency between interdependent data. The design of two concurrency control mechanisms for concurrent execution of polytransactions is given. The first is a deadlock-free graph-locking mechanism and the second is a variant of multiversion timestamps with rollback that never rejects operations arriving out of timestamp order. A conceptual system architecture is outlined for the execution of polytransactions. The notion of a multidatabase monitor is discussed.<<ETX>>