Anany Levitin

The notion of data and its quality dimensions

Abstract The rapid proliferation of computer-based information systems is increasing the importance of data quality to both system makers and users. However, there is neither an established framework nor common terminology for investigating data quality. There is not even agreement on what the term “data” means. We lay a foundation for the study of data quality in this paper. In the first part of the paper we discuss five approaches to defining “data” in the literature. We then propose an approach especially conducive to discussing data quality. In the second part of the paper we discuss the most important dimensions of data quality: accuracy, completeness, consistency and currentness. We define these four and several related dimensions and discuss them in detail. We close the paper by outlining several areas for further research on data quality.

Quality dimensions of a conceptual view

Abstract Data quality is usually associated with the quality of data values. But even perfectly correct data values are of little use if they are based on a deficient data model. The purpose of this paper is to present and discuss a list of characteristics (dimensions) that are crucial for data model quality. We single out 14 quality dimensions, organized into six categories: content, scope, level of detail, composition, consistency, and reaction to change. Two types of correlation among dimensions called “reinforcements” and “tradeoffs” are recognized and discussed as well.

Using puzzles in teaching algorithms

This paper advocates a wider use of puzzles and puzzle-like problems in teaching design and analysis of algorithms. It discusses a variety of puzzles and classifies them according to the general algorithm design techniques. Pedagogic issues are explored.

A model of the data (life) cycles with application to quality

Abstract The purpose of the paper is to present a new model of the data life-cycle. Such a model is needed to clarify activities involving data, from its creation through use, and to establish the relationships of these activities to one another. The proposed model features four principal data cycles: the acquisition cycle includes activities that create and store data, the usage cycle includes activities that retrieve and use data, and the two kinds of the combined cycles incorporate both acquisition and usage activities. The model also includes quality checkpoints and feedback loops. These are particularly useful in clarifying data quality issues.

Do we teach the right algorithm design techniques

Algorithms have come to be recognized as the cornerstone of computing. Surprisingly, there has been little research or discussion of general techniques for designing algorithms. Though several such techniques have been identified, there are serious shortcomings in the existing taxonomy. The paper points out these shortcomings, reevaluates some of the techniques, and proposes a new, hierarchical classification scheme by grouping techniques according to their level of generality. A variety of examples from different areas of computing are used to demonstrate the power and flexibility of the taxonomy being proposed.

Analyze that: puzzles and analysis of algorithms

The paper advocates a wider use of puzzles and puzzle-like games in teaching the analysis of algorithms. It discusses many specific examples---from classic puzzles of recreational mathematics to newly popular job interview brainteasers---which illustrate all major aspects of algorithm analysis.

Design and analysis of algorithms reconsidered

The paper elucidates two views (models) of algorithmic problem solving. The first one is static; it is based on the identification of several principal dimensions of algorithmic problem solving. The second one is dynamic, i.e., it catalogs main steps in the process of solving a problem with a computer. The models are used to identify several important issues in teaching design and analysis of algorithms and to suggest ways of rectifying the shortcomings identified.

SoSE Modeling and Simulation Approaches to Evaluate Security and Performance Limitations of a Next Generation National Healthcare Information Network (NHIN-2)

The emerging national healthcare information network (NHIN) is intended to improve the efficacy, efficiency, and safety of healthcare. At the same time. Service oriented architectures (SOAs) are rapidly becoming an accepted means of providing network information exchange across a heterogeneous fabric of node, and may be suitable for a next-generation NHIN-2s. Given the complexity NHIN and SOA due to multiple levels of system interactions, creating a valid and usable SoSE model for SOA application using a single technique that captures the desired level of details can be a daunting task. In this paper we give details of a hybrid approach to modeling and simulation of a specific SOA that is named MCSOA and has been configured for potential defense applications. Further, we show how MCSOA could be used to link low-comnmunications-capability healthcare data from sources like, the Alaska telemedicine testbed project (ATTP) to the proposed NHIN-2.

Digging for algorithmic nuggets in the land of polyominoes

This paper seeks to demonstrate that tiling with polyominoes, almost entirely neglected by textbooks on algorithms, can provide valuable examples of applications of algorithm design techniques, some of which are not easy to illustrate by examples from other domains.