Subhasish Mazumdar

Achieving consistency in mobile databases through localization in PRO-MOTION

There is great need and potential for traditional transaction support in a mobile computing environment. However owing to the inherent limitations of mobile computing, we need to augment the well-developed techniques of database management systems with new approaches. We focus on the challenge of assuring data consistency. Our approach of localization is to reformulate global constraints so as to enhance the autonomy of the mobile hosts. We show how this approach unifies techniques of maintaining replicated data with methods of enforcing polynomial inequalities. We also discuss how localization can be implemented in PRO-MOTION, a flexible infrastructure for transaction processing in a mobile environment.

Localization of Integrity Constraints in Mobile Databases and Specification in PRO-MOTION

The well-developed traditional data management techniques need to be augmented with new approaches in order to continue to be effective in the mobile environment. In this paper, we focus on the challenge of maintaining integrity constraints in the presence of disconnections and expensive communication. Our approach of localization is to reformulate global constraints so as to enhance the autonomy of the mobile hosts in processing transactions. We show how this approach unifies techniques of maintaining replicated data with methods of enforcing polynomial inequalities. We also discuss how localization can be realized in PRO-MOTION, a flexible infrastructure for transaction processing in a mobile environment.

Caching constrained mobile data

As mobile devices get ubiquitous and grow in computational power, their management of interdependent data also becomes increasingly important. The mobile environment exhibits all the characteristics of a distributed database plus the feature of whimsical connectivity. Consequently, transactions respecting data consistency can suffer unbounded and unpredictable delays at both mobile and stationary nodes. The currently popular multi-tier model, in which mobile devices are in one end and always-connected stationary servers in the other, has certain practical advantages. However, it assumes that all integrity constraints are evaluated at the servers and hence relies on the semantics of operations for any autonomy enhancement of the mobile devices. In this paper, we examine the idea of constraint localization in cases where two mobile nodes each own data that share a constraint. It relies on reformulation of a constraint into more flexible local constraints that give more autonomy to the mobile nodes. The scheme also involves dynamic changes of these local constraints through negotiation, which we call re-localization. To overcome the problem of simultaneous requests for such re-localization, we give algorithms along with experimental results indicating their effectiveness.

Supervised inductive learning with Lotka–Volterra derived models

We present a classification algorithm built on our adaptation of the Generalized Lotka–Volterra model, well-known in mathematical ecology. The training algorithm itself consists only of computing several scalars, per each training vector, using a single global user parameter and then solving a linear system of equations. Construction of the system matrix is driven by our model and based on kernel functions. The model allows an interesting point of view of kernels’ role in the inductive learning process. We describe the model through axiomatic postulates. Finally, we present the results of the preliminary validation experiments.

Realizing Travel Malls: A Logic Programming Based Approach

The vision of a Travel Mall, if fully realized, would allow the tourism industry to come to grips with the potential of the advances in information and communication technologies as well as the needs and demands of customers in this new age. In order to realize this vision, certain issues need to be addressed; that of information integration is crucial. We have presented an architecture for a travel mall that tackles this problem and addresses all the important issues. We outline how our approach exploits an extension of logic programming.

Can Spatial Transformation-Based Privacy Preservation Compromise Location Privacy?

While mobile users would like to pose location-based queries such as “find me the nearest service of type S” or “find me k nearest services of type S,” they are increasingly aware of the underlying privacy concerns and threats. One of the two main approaches suggested by researchers for countering this threat involves spatial transformation via Hilbert curves. This scheme transforms a two-dimensional coordinate space into a one-dimensional space such that adjacency in the latter space implies contiguity in the former. A Trusted Server (TS) provides a Location-Based Server (LBS) with Points of Interest (POIs) indexed by the one-dimension Hilbert indices obtained by transforming their two-dimensional coordinates; mobile users query the LBS using the Hilbert index corresponding to their two-dimensional coordinates; the LBS finds the nearest Hilbert indices with appropriate POIs and returns them to the users. Owing to the large number of possible Hilbert transformations using different parameters, the attempt by an LBS to invert the transformation, i.e., compute a two-dimensional coordinate of the target user (or the POIs) is generally considered infeasible. In this paper, we ask to what extent and by which methods can a rogue LBS squeeze a privacy compromise from this scheme? We model the adversary and examine the possibilities of an attack based on semantic factors such as the distribution of POI categories and variations in POI density as well as collusion and exploitation of weaknesses in the mobile system software architecture. Finally, we point out limitations of such attacks and suggest strategies to strengthen defences against them.

A Document-Based Approach to Monitor Business Process Instances

Keeping track of business process instances is needed for better management, especially queriability and monitorability, of the enterprise as a whole. The business process instances’ unpredictable behaviour makes this tracking even more necessary. Currently, this information is not completely or explicitly maintained. We propose a model that captures the states of business process instances by keeping track of their informational access operations (within/outside the scope of automated management). This model is based on an information model that views information within the enterprise as a set of documents and keeps an up-to-date capture of this information model. These models can then be the underlying models for an automated system that keeps track of the states of business process instances and makes this information efficiently queriable.

Limiting Answers to Queries to Enhance Security of Mobile Database

While mobile computing has seen tremendous growth and popularity, it has also introduced vulnerabilities in information systems. When a mobile personal computing device is stolen or misplaced, a great amount of data obtained from database servers can be compromised; hence, it is useful to limit the amount of sensitive data on mobile clients. In a number of applications, it is necessary to limit the amount of answers in response to a user query in order to enhance the security of a database; for example, an army base can answer queries asking for the phone numbers of its residents and yet, it should not reveal the whole book. Since databases are large and dynamic in content and structure, and the results of queries are unpredictable, it is not feasible to manually specify exactly which tuple should be suppressed for which user. In this paper, our approach is based on declarative specifications: the Database Administrator specifies the secrecies, i.e., the queries whose answers need to be limited, and the user privileges, i.e., the number of tuples that can be revealed when a user query intersects with a secrecy. The output of every query that intersects with one of the secrecies will be limited in the number of tuples revealed.

A modeling-based classification algorithm validated with simulated data

We present a Generalized Lotka-Volterra (GLV) based approach for modeling and simulation of supervised inductive learning, and construction of an efficient classification algorithm. The GLV equations, typically used to explain the biological world, are employed to simulate the process of inductive learning. In addition, the modeling approach provides a key advantage over the more conventional constraint and optimization-based classification algorithms, as influences of outliers and local patterns, which can lead to problematic overfitting, are auto-moderated by the model itself. We present the bare-bones algorithm and motivate the model through axiomatic postulates. The algorithm is validated using benchmark simulated datasets, showing results competitive with other cutting-edge algorithms.

Accommodating streams to support active conceptual modeling of learning from surprises

We argue that a key requirement on an information system that can implement an active conceptual model of learning from surprises is the ability to query data that is not query-able by content, especially data streams; we suggest that such data be queried by context. We propose an enhancement of entity-relationship modeling with active constructs in order to permit such streams to have context-based relationships with standard data. We propose a framework where in the analysis of surprises and the subsequent monitoring of states that are ripe for such events are possible by the use of such contexts.