In this paper, we study the problem of keyword proximity search over XML documents and leverage the efficiency and effectiveness. We take the disjunctive semantics among input keywords into consideration and identify meaningful compact connected trees as the answers of keyword proximity queries. We introduce the notions of Compact Lowest Common Ancestor (CLCA) and Maximal CLCA (MCLCA) and propose Compact Connected Trees (CCTrees) and Maximal CCTrees (MCCTrees) to efficiently and effectively answer keyword queries. We propose a novel ranking mechanism, RACE, to Rank compAct Connected trEes, by taking into consideration both the structural similarity and the textual similarity. Our extensive experimental study shows that our method achieves both high search efficiency and effectiveness, and outperforms existing approaches significantly.

Conditional functional dependencies (CFDs) have recently been proposed as a useful integrity constraint to summarize data semantics and identify data inconsistencies. A CFD augments a functional dependency (FD) with a pattern tableau that defines the context (i.e., the subset of tuples) in which the underlying FD holds. While many aspects of CFDs have been studied, including static analysis and detecting and repairing violations, there has not been prior work on generating pattern tableaux, which is critical to realize the full potential of CFDs. This paper is the first to formally characterize a "good" pattern tableau, based on naturally desirable properties of support, confidence and parsimony. We show that the problem of generating an optimal tableau for a given FD is NP-complete but can be approximated in polynomial time via a greedy algorithm. For large data sets, we propose an "on-demand" algorithm providing the same approximation bound, that outperforms the basic greedy algorithm in running time by an order of magnitude. For ordered attributes, we propose the range tableau as a generalization of a pattern tableau, which can achieve even more parsimony. The effectiveness and efficiency of our techniques are experimentally demonstrated on real data.

The wide popularity of free-and-easy keyword based searches over World Wide Web has fueled the demand for incorporating keyword-based search over structured databases. However, most of the current research work focuses on keyword-based searching over a single structured data source. With the growing interest in distributed databases and service oriented architecture over the Internet, it is important to extend such a capability over multiple structured data sources. One of the most important problems for enabling such a query facility is to be able to select the most useful data sources relevant to the keyword query. Traditional database summary techniques used for selecting unstructured datasources developed in IR literature are inadequate for our problem, as they do not capture the structure of the data sources. In this paper, we study the database selection problem for relational data sources, and propose a method that effectively summarizes the relationships between keywords in a relational database based on its structure. We develop effective ranking methods based on the keyword relationship summaries in order to select the most useful databases for a given keyword query. We have implemented our system on PlanetLab. In that environment we use extensive experiments with real datasets to demonstrate the effectiveness of our proposed summarization method.

This paper introduces the use of wavelets for information retrieval in a peer-to-peer environment. In order to achieve our purposes, we use a new combination between broadcasting and a hierarchical overlay. Compared to previous approaches, we do not store complete information about the children of a super-peer, nor do we broadcast the queries blindly. We approximate the feature vectors using the multiresolution analysis and the discrete wavelet transform. Each peer is represented by a high-dimensional feature vector and the height of the hierarchy is logarithmic in the dimensionality of this feature vector. Leaf nodes represent real peers, while internal nodes are virtual peers used for routing. Our retrieval method has been tested with both real and synthetic data and shown to be efficient in retrieving relevant information, resulting in good precision and recall on four standard test collections.

TaintTrace is a high performance flow tracing tool that protects systems against security exploits. It is based on dynamic execution binary rewriting empowering our tool with fine-grained monitoring of system activities such as the tracking of the usage and propagation of data originated from the network. The challenge lies in minimizing the run-time overhead of the tool. TaintTrace uses a number of techniques such as direct memory mapping to optimize performance. In this paper, we demonstrate that TaintTrace is effective in protecting against various attacks while maintaining a modest slowdown of 5.5 times, offering significant improvements over similar tools.

Most existing peer-to-peer (P2P) systems support only title-based searches and are limited in functionality when compared to today's search engines. We present the design of a distributed P2P information sharing system that supports semantic-based content searches of relevant documents. First, we propose a general and extensible framework for searching similar documents in P2P network. The framework is based on the novel concept of hierarchical summary structure. Second, based on the framework, we develop our efficient document searching system by effectively summarizing and maintaining all documents within the network with different granularity. Finally, an experimental study is conducted on a real P2P prototype, and a large-scale network is further simulated. The results show the effectiveness, efficiency, and scalability of the proposed system.