Aryya Gangopadhyay

A privacy-preserving technique for Euclidean distance-based mining algorithms using Fourier-related transforms

Privacy preserving data mining has become increasingly popular because it allows sharing of privacy-sensitive data for analysis purposes. However, existing techniques such as random perturbation do not fare well for simple yet widely used and efficient Euclidean distance-based mining algorithms. Although original data distributions can be pretty accurately reconstructed from the perturbed data, distances between individual data points are not preserved, leading to poor accuracy for the distance-based mining methods. Besides, they do not generally focus on data reduction. Other studies on secure multi-party computation often concentrate on techniques useful to very specific mining algorithms and scenarios such that they require modification of the mining algorithms and are often difficult to generalize to other mining algorithms or scenarios. This paper proposes a novel generalized approach using the well-known energy compaction power of Fourier-related transforms to hide sensitive data values and to approximately preserve Euclidean distances in centralized and distributed scenarios to a great degree of accuracy. Three algorithms to select the most important transform coefficients are presented, one for a centralized database case, the second one for a horizontally partitioned, and the third one for a vertically partitioned database case. Experimental results demonstrate the effectiveness of the proposed approach.

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.

Managing uncertainty in location services using rough set and evidence theory

Abstract Uncertainty in service management stems from the incompleteness and vagueness of the conditioning attributes that characterize a service. In particular, location based services often have complex interaction mechanisms in terms of their neighborhood relationships. Classical location service models require rigorous parameters and conditioning attributes and offers limited flexibility to incorporate imprecise or ambiguous evidences. In this paper we have developed a formal model of uncertainty in service management. We have developed a rough set and Dempster–Shafer’s evidence theory based formalism to objectively represent uncertainty inherent in the process of service discovery, characterization, and classification. Rough set theory is ideally suited for dealing with limited resolution, vague and incomplete information, while Dempster–Shafer’s evidence theory provides a consistent approach to model an expert’s belief and ignorance in the classification decision process. Integrating these two formal approaches in spatial domain provides a way to model an expert’s belief and ignorance in service classification. In an application scenario of the model we have used a cognitive map of retail site assessment, which reflects the partially subjective assessment process. The uncertainty hidden in the cognitive map can be consistently formalized using the proposed model. Thus we provide a naturalistic means of incorporating both qualitative aspects of intuitive knowledge as well as hard empirical information for service management within a formal uncertainty framework.

A Simulation Study of Supply Chain Management to Measure the Impact of Information Sharing

It has been found that supply chain collaboration has a significant impact on the ability of an organization to meet customer needs and reduce costs. A key step in supply chain collaboration is sharing information among supply chain partners. In this paper a simulation study is presented to investigate the effectiveness of information sharing. The results show that from the perspectives of end inventory and back-order quantities, distributors and wholesalers gain significantly from information sharing, while not much gain can be realized for retailers.

A Privacy Protection Model for Patient Data with Multiple Sensitive Attributes

The identity of patients must be protected when patient data are shared. The two most commonly used models to protect identity of patients are L-diversity and K-anonymity. However, existing work mainly considers data sets with a single sensitive attribute, while patient data often contain multiple sensitive attributes (e.g., diagnosis and treatment). This article shows that although the K-anonymity model can be trivially extended to multiple sensitive attributes, the L-diversity model cannot. The reason is that achieving L-diversity for each individual sensitive attribute does not guarantee L-diversity over all sensitive attributes. We propose a new model that extends L-diversity and K-anonymity to multiple sensitive attributes and propose a practical method to implement this model. Experimental results demonstrate the effectiveness of our approach.

Design and Implementation of a Web-Based Collaborative Spatial Decision Support System: Organizational and Managerial Implications

The development of collaborative spatial decision support systems presents a host of challenges, ranging from technical to societal and institutional. Resource managers and environmental planners often need to understand the effect of the distributed and uncoordinated land management practices of individual decision-makers, which in the long run causes significant environmental impact. In many cases environmental planning requires collaborative decision-making tools where complex interacting agents with conflicting goals need to work without any prior idea of the counterpart. This paper identifies research issues on the design and implementation of a Web-based collaborative spatial decision making in the specific context of distributed environmental planning. We demonstrate a Web-based Spatial Decision Support System GEO-ELCA Exploratory Land Use Change Assessment for typical decision-making tasks by urban or municipal planning agencies where resource managers or stakeholders of different interest groups can express their options for future land use changes and assess the resulting hydrological impacts in a collaborative environment.

A user-centered design for a spatial data warehouse for data exploration in environmental research

Abstract The integration of data from diverse fields of ecological research is paramount in the discovery of new ecological patterns and processes. The spatial exploration of an integrated dataset that spans multiple studies and disciplines can allow researchers to gain unforeseen insight into their data, spawn new research questions and hypotheses and identify data gaps. A user-centered approach was taken to design a spatial data warehouse and online analytical processing (OLAP) tools for data exploration in ecological research. The users in this study had diverse needs and current methods of data management do not easily allow for integration and exploration of data in multidimensional space. A generalizable data warehouse design methodology was created based on the results of a user study. This methodology was then demonstrated in the design of a data warehouse for data exploration in stream ecology resulting in a multidimensional data model with a fact table representing biological stream survey measurements and dimension tables representing spatial and categorical site and landscape variables. A generalizable extraction transformation and loading (ETL) workflow was created to integrate data across spatial dimensions before it was loaded into the data warehouse. A prototype data warehouse was implemented using biological stream survey, hydrologic, and vegetation data to observe spatial patterns in biological community distributions. Based on the exploration requirements identified in the user study, prototype OLAP queries were designed to facilitate spatial data cube exploration. Finally, a web-based interface was implemented to allow for multidimensional spatial visualization of biological stream survey data. The data warehouse and interface will allow researchers to explore biological assessment data at multiple spatial scales across many dimensions.

Database Issues in Geographic Information Systems

From the Publisher: Database Issues in Geographic Information Systems approaches several important topics in GIS from a database perspective. Database management has a central role to play in most computer-based information systems, and is expected to have an equally important role to play in managing information in GIS as well. Existing database technology, however, focuses on the alphanumeric data that are required in business applications. GIS, like many other application areas, requires the ability to handle spatial as well as alphanumeric data. This requires new innovations in data management, which is the central theme of this monograph. Database Issues in Geographic Information Systems is suitable as a secondary text for a graduate level course on Geographic Information Systems, Database Systems or Cartography, and as a reference for researchers and practitioners in industry.

Conceptual modeling from natural language functional specifications

Abstract In this paper we describe a structured method for developing a conceptual data model by starting from a functional model expressed in a natural language. We have used the Conceptual Dependency theory for mapping natural language descriptions to conceptual dependency diagrams. We have developed algorithms to convert these conceptual dependency diagrams into unit conceptual dependency tables, which are then merged to represent the whole context of the application. We also show how transactional requirements can be incorporated into the unit conceptual dependency table, and subsequently convert the unit conceptual dependency table into a corresponding conceptual model. We have developed an augmented transition network (ATN) parser to develop conceptual dependency diagrams from natural language descriptions. A prototype system has been implemented using Oracle8i and developer platforms.

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.