Rakesh M. Verma

An efficient multiversion access structure

An efficient multiversion access structure for a transaction-time database is presented. Our method requires optimal storage and query times for several important queries and logarithmic update times. Three version operations-inserts, updates, and deletes-are allowed on the current database, while queries are allowed on any version, present or past. The following query operations are performed in optimal query time: key range search, key history search, and time range view. The key-range query retrieves all records having keys in a specified key range at a specified time; the key history query retrieves all records with a given key in a specified time range; and the time range view query retrieves all records that were current during a specified time interval. Special cases of these queries include the key search query, which retrieves a particular version of a record, and the snapshot query which reconstructs the database at some past time. To the best of our knowledge no previous multiversion access structure simultaneously supports all these query and version operations within these time and space bounds. The bounds on query operations are worst case per operation, while those for storage space and version operations are (worst-case) amortized over a sequence of version operations. Simulation results show that good storage utilization and query performance is obtained.

A Query-Based Medical Information Summarization System Using Ontology Knowledge

As huge amounts of knowledge are created rapidly, effective information access becomes an important issue. Especially for critical domains, such as medical and financial areas, efficient retrieval of concise and relevant information is highly desired. In this paper we propose a new user query based text summarization technique that makes use of unified medical language system, an ontology knowledge source from National Library of Medicine. We compare our method with keyword-only approach, and our ontology-based method performs clearly better. Our method also shows potential to be used in other information retrieval areas

Algorithms and Reductions for Rewriting Problems

In this paper we initiate a study of polynomial-time reductions for some basic decision problems of rewrite systems. We then give a polynomial-time algorithm for the unique-normal-form property of ground systems for the first time. Next we prove undecidability of several problems for a fixed string rewriting system using our reductions. Finally, we prove the decidability of confluence for commutative semi-thue systems. The Confluence and Unique-normal-form property are shown Expspace-hard for commutative semi-thue systems. We also show that there is a family of string rewrite systems for which the word problem is trivially decidable but confluence is undecidable, and we show a linear equational theory with decidable word problem but undecidable linear equational matching problem.

Tight bounds for prefetching and buffer management algorithms for parallel I/O systems

The I/O performance of applications in multiple-disk systems can be improved by overlapping disk accesses. This requires the use of appropriate prefetching and buffer management algorithms that ensure the most useful blocks are accessed and retained in the buffer. In this paper, we answer several fundamental questions on prefetching and buffer management for distributed-buffer parallel I/O systems. First, we derive and prove the optimality of an algorithm, P-min, that minimizes the number of parallel I/Os. Second, we analyze P-con, an algorithm that always matches its replacement decisions with those of the well-known demand-paged MIN algorithm. We show that P-con can become fully sequential in the worst case. Third, we investigate the behavior of on-line algorithms for multiple-disk prefetching and buffer management. We define and analyze P-Iru, a parallel version of the traditional LRU buffer management algorithm. Unexpectedly, we find that the competitive ratio of P-Iru is independent of the number of disks. Finally, we present the practical performance of these algorithms on randomly generated reference strings. These results confirm the conclusions derived from the analysis on worst case inputs.

RPO Constraint Solving Is in NP

A new decision procedure for the existential fragment of ordering constraints expressed using the recursive path ordering is presented. This procedure is nondeterministic and checks whether a set of constraints is solvable over the given signature, i.e., the signature over which the terms in the constraints are defined. It is shown that this non-deterministic procedure runs in polynomial time, thus establishing the membership of this problem in the complexity class NP for the first time.

Local and Symbolic Bisimulation Using Tabled Constraint Logic Programming

Bisimulation is a fundamental notion that characterizes behavioral equivalence of concurrent systems. In this paper, we study the problem of encoding efficient bisimulation checkers for finite- as well as infinite-state systems as logic programs. We begin with a straightforward andshort (less than 10 lines) encoding of finite-state bisimulation checker as a tabledlogic program. In a goal-directedsystem like XSB, this encoding yields a local bisimulation checker: one where state space exploration is done only until a dissimilarity is revealed. More importantly, the logic programming formulation of local bisimulation can be extended to do symbolic bisimulation for checking the equivalence of infinite-state concurrent systems representedb y symbolic transition systems. We show how the two variants of symbolic bisimulation (late andearly equivalences) can be formulatedas a tabledconstrain t logic program in a way that precisely brings out their differences. Finally, we show that our symbolic bisimulation checker actually outperforms non-symbolic checkers even for relatively small finite-state systems.

An analysis of a good algorithm for the subtree problem, correlated

It is shown that the proof of the main result in Reyner’s paper, similarly titled, is incorrect. Interestingly, by combining a simple modification of the algorithm with tighter analysis, one can obtain the original result with a minor improvement.

Tight complexity bounds for term matching problems

Abstract In this paper we study the sequential and parallel complexity of various important term matching problems. These problems occur frequently in applications such as term rewriting, functional programming, and logic programming. First, we obtain non-trivial lower bounds on the parallel complexity of the (uninterpreted) term matching problem. We also establish the tightness of these bounds for some representations and several models. We then characterize completely the sequential complexity of associative and commutative matching when the number of occurrences of variables is varied. Specifically, we show that even if each variable is restricted to at most two occurrences in the terms, both associative matching and commutative matching are NP-complete. Interestingly, for the important restriction of boolean terms we show that commutative matching is NP-complete whereas associative matching has a linear time sequential algorithm. For linear terms, we significantly improve the existing upper bound for associative-commutative matching, and present a new algorithm for associative matching. Designing direct parallel algorithms for associative-commutative matching of linear terms appears to be a difficult task. Despite this we have been able to resolve questions about the parallel complexity of these problems using complexity-theoretic techniques. Finally, an interesting consequence of the research reported here is the demonstration of a tighter relationship between associative-commutative matching for linear terms and bipartite matching on both sequential and parallel models.

Detecting Phishing Emails the Natural Language Way

Phishing causes billions of dollars in damage every year and poses a serious threat to the Internet economy. Email is still the most commonly used medium to launch phishing attacks [1]. In this paper, we present a comprehensive natural language based scheme to detect phishing emails using features that are invariant and fundamentally characterize phishing. Our scheme utilizes all the information present in an email, namely, the header, the links and the text in the body. Although it is obvious that a phishing email is designed to elicit an action from the intended victim, none of the existing detection schemes use this fact to identify phishing emails. Our detection protocol is designed specifically to distinguish between “actionable” and “informational” emails. To this end, we incorporate natural language techniques in phishing detection. We also utilize contextual information, when available, to detect phishing: we study the problem of phishing detection within the contextual confines of the user’s email box and demonstrate that context plays an important role in detection. To the best of our knowledge, this is the first scheme that utilizes natural language techniques and contextual information to detect phishing. We show that our scheme outperforms existing phishing detection schemes. Finally, our protocol detects phishing at the email level rather than detecting masqueraded websites. This is crucial to prevent the victim from clicking any harmful links in the email. Our implementation called PhishNet-NLP, operates between a user’s mail transfer agent (MTA) and mail user agent (MUA) and processes each arriving email for phishing attacks even before reaching the inbox.

On the Character of Phishing URLs: Accurate and Robust Statistical Learning Classifiers

Phishing attacks resulted in an estimated