Nematollaah Shiri

A parametric approach to deductive databases with uncertainty

Numerous frameworks have been proposed in recent years for deductive databases with uncertainty. On the basis of how uncertainty is associated with the facts and rules in a program, we classify these frameworks into implication-based (IB) and annotation-based (AB) frameworks. We take the IB approach and propose a generic framework, called the parametric framework, as a unifying umbrella for IB frameworks. We develop the declarative, fixpoint, and proof-theoretic semantics of programs in our framework and show their equivalence. Using the framework as a basis, we then study the query optimization problem of containment of conjunctive queries in this framework and establish necessary and sufficient conditions for containment for several classes of parametric conjunctive queries. Our results yield tools for use in the query optimization for large classes of query programs in IB deductive databases with uncertainty.

An Efficient Technique for Mining Usage Profiles Using Relational Fuzzy Subtractive Clustering

We propose an efficient technique for mining web usage profiles based on subtractive clustering that scales to large datasets. Unlike earlier clustering based techniques for the same purpose, our technique does not require user specification of any input parameter to obtain the desired clustering. Instead, we achieve this by searching in the cluster space for the best clustering of the given web usage data. To evaluate clustering quality, we have formulated a validity index for our algorithm. Our implementation of the proposed technique and the experiments with large real life datasets show that it indeed mines the desired usage profiles much faster than existing techniques.

Formalization of RBAC policy with object class hierarchy

Formal methods and reasoning techniques can be useful tools for the representation and analysis of security policies and access control procedures. This paper presents a logical approach to representing and evaluating role-based access control (RBAC) policies, using description logics and a proof method, called tableaux. We propose a new variation of the RBAC model with a classification mechanism for objects. The key feature supported is the ability to model object classes, and class hierarchies used to restrict the validity and to control the propagation of authorization rules. We also demonstrate how access control decisions are made by tableaux, considering role and class hierarchies.

Measuring Inconsistencies in Ontologies

In this paper, we propose a novel approach to measure inconsistencies in ontologies based on Shapley values, which are originally proposed for game theory. This measure can be used to identify which axioms in an input ontology or which parts of these axioms need to be removed or modified in order to make the input consistent. We also propose optimization techniques to improve the efficiency of computing Shapley values. The proposed approach is independent of a particular ontology language or a particular reasoning system used. Application of this approach can improve the quality of ontology diagnosis and repair in general.

An effective ontology matching technique

In this paper, we study the ontology matching problem and propose an algorithm, which uses as a backbone a multi-level matching technique and performs a neighbor search to find the correspondences between the entities in the given ontologies. A main feature of this algorithm is the high quality of the matches it finds. Besides, as the result of the initial search introduced, our algorithm converges fast, making it comparable to existing techniques.

An empirical comparison of ontology matching techniques

Ontology matching aims to find semantic correspondences between a pair of input ontologies. A number of matching techniques have been proposed recently. We may, however, benefit more from a combination of such techniques as opposed to just a single method. This is more appropriate, but very often the user has no prior knowledge about which technique is more suitable for the task at hand, and it remains a labour intensive and expensive task to perform. Further, the complexity of the matching process as well as the quality of the result is affected by the choice of the applied matching techniques. We study this problem and propose a framework for finding suitable matches. A main feature of this is that it improves the structure matching techniques and the end result accordingly. We have developed a running prototype of the proposed framework and conducted experiments to compare our results with existing techniques. While being comparable in efficiency, the experimental results indicate our proposed technique produces better quality matches.

A compact multi-resolution index for variable length queries in time series databases

We study the problem of searching similar patterns in time series data for variable length queries. Recently, a multi-resolution indexing technique (MRI) was proposed in (Kahveci and Singh, in proceedings of the international conference on data engineering, pp. 273–282, 2001; Kahveci and Singh, IEEE Trans Knowl Data Eng 16(4):418–433, 2004) to address this problem, which uses compression as an additional step to reduce the index size. In this paper, we propose an alternative technique, called compact MRI (CMRI), which uses adaptive piecewise constant approximation (APCA) representation as dimensionality reduction technique, and which occupies much less space without requiring compression. We implemented both MRI and CMRI, and conducted extensive experiments to evaluate and compare their performance on real stock data as well as synthetic. Our results indicate that CMRI provides a much better precision ranging from 0.75 to 0.89 on real data, and from 0.80 to 0.95 on synthetic data, while for MRI, these ranges are from 0.16 to 0.34, and from 0.03 to 0.65, respectively. Compared to sequential scan, we found that CMRI is 4–30 times faster and the number of disk I/Os it required is close to minimal. In terms of storage utilization, CMRI occupies 1% of the memory occupied by MRI. These results and analysis show CMRI to be an efficient and scalable indexing technique for large time series databases.

Improving the Effectiveness of Model Based Recommender Systems for Highly Sparse and Noisy Web Usage Data

A number of approaches which use model-based collaborative filtering (CF) for scalability in building recommendation systems in Web personalization have poor accuracy due to the fact that Web usage data is often sparse and noisy. Clustering, mining association rules, and sequence pattern discovery have been used to determine the access behavior model. Making use of some of the characteristics of the modeling process can provide significant improvements to recommendation effectiveness. In an earlier work, we introduced a fuzzy hybrid CF technique which inherits the advantages of both memory-based and model-based CF. In this paper, using relational fuzzy subtractive clustering as the first level modeling and then mining association rules within individual clusters, we propose a two level model-based technique, which is scalable and is an enhancement over association rule based recommender systems. Our results from comprehensive experiments using a large real life Web usage data and performance comparisons with memory-based and model-based approaches help substantiate this claim.

Languages for multi-database interoperability

Database system technology which there is a moliferation has reached a of inderrendent stage now in svstems storing and manipul~ting enormous amount of data. Unfortunately, these systems typically have their own data models, communication processing protocols, query processing systems, concurrency control protocols, consistency management, and other similar aspects of database systems. There is also an increasing need for In teroperability among these systems. Though considerable amount of research has been done in the area of database interoperability, most of it has resulted in solutions that are ad-hoc and procedural. We have developed a declarative environment in which multiple heterogeneous databases interoperate by sharing, interpreting, and manipulating information, in a uniform way. An important criterion for Interacting with multiple databases is the abiIity to query them in a manner independent of the discrepancies in their structure and data semantics. In this demo, we exhibk two languages for querying across multiple databaaes which store semantically similar data using heterogeneous schema, ss well as for restructuring the queried data: (1) SchemaLog – a language that has its foundations in logic and (2) SchemaSQL – a language based on a principled extension to SQL.

Uncertainty Reasoning for Ontologies with General TBoxes in Description Logic

We present a reasoning procedure for ontologies with uncertainty described in Description Logic (DL) which include General TBoxes, i.e., include cycles and General Concept Inclusions (GCIs). For this, we consider the description language