On processing nested queries in distributed object-oriented database systems
In this paper, we discuss nested query processing in a distributed object-oriented database system. We present three query processing strategies to exploit parallelism in a distributed environment. Then, we review three access methods designed for centralized systems and discuss how they can be applied to the distributed environment. Heuristics for selecting attributes for indexing and determining where to store the indexes are also presented. Finally, a replication strategy is proposed for the path dictionary method.<<ETX>>

title: Approaching the Skyline in Z Order
abstract: Given a set of multidimensional data points, skyline query retrieves a set of data points that are not dominated by any other points. This query is useful for multi-preference analysis and decision making. By analyzing the skyline query, we observe a close connection between Z-order curve and skyline processing strategies and propose to use a new index structure called ZBtree, to index and store data points based on Z-order curve. We develop a suite of novel and efficient skyline algorithms, which scale very well to data dimensionality and cardinality, including (1) ZSearch, which processes skyline queries and supports progressive result delivery; (2) ZUpdate, which facilitates incremental skyline result maintenance; and (3) k-ZSearch, which answers k-dominant skyline query (a skyline variant that retrieves a representative subset of skyline results). Extensive experiments have been conducted to evaluate our proposed algorithms and compare them against the best available algorithms designed for skyline search, skyline result update, and k-dominant skyline search, respectively. The result shows that our algorithms, developed coherently based on the same ideas and concepts, soundly outperforms the state-of-the-art skyline algorithms in their specialized domains.

title: Power conservative multi-attribute queries on data broadcast
abstract: Studies power conservation techniques for multi-attribute queries on wireless data broadcast channels. Indexing data on broadcast channels can improve the client filtering capability, while clustering and scheduling can reduce both the access time and the tune-in time. Thus, indexing techniques should be coupled with clustering and scheduling methods to reduce the battery power consumption of mobile computers. In this study, three indexing schemes for multi-attribute queries, namely the index tree, signature and hybrid index, are discussed. We develop cost models for these three indexing schemes and evaluate their performance based on multi-attribute queries on wireless data broadcast channels.

title: Top-k Monitoring in Wireless Sensor Networks
abstract: Top-k monitoring is important to many wireless sensor applications. This paper exploits the semantics of top-k query and proposes an energy-efficient monitoring approach called FILA. The basic idea is to install a filter at each sensor node to suppress unnecessary sensor updates. Filter setting and query reevaluation upon updates are two fundamental issues to the correctness and efficiency of the FILA approach. We develop a query reevaluation algorithm that is capable of handling concurrent sensor updates. In particular, we present optimization techniques to reduce the probing cost. We design a skewed filter setting scheme, which aims to balance energy consumption and prolong network lifetime. Moreover, two filter update strategies, namely, eager and lazy, are proposed to favor different application scenarios. We also extend the algorithms to several variants of top-k query, that is, order-insensitive, approximate, and value monitoring. The performance of the proposed FILA approach is extensively evaluated using real data traces. The results show that FILA substantially outperforms the existing TAG-based approach and range caching approach in terms of both network lifetime and energy consumption under various network configurations.

title: An error-resilient and tunable distributed indexing scheme for wireless data broadcast
abstract: Access efficiency and energy conservation are two critical performance concerns in a wireless data broadcast system. We propose in this paper a novel parameterized index called the exponential index that has a linear yet distributed structure for wireless data broadcast. Based on two tuning knobs, index base and chunk size, the exponential index can be tuned to optimize the access latency with the tuning time bounded by a given limit, and vice versa. The client access algorithm for the exponential index under unreliable broadcast is described. A performance analysis of the exponential index is provided. Extensive ns-2-based simulation experiments are conducted to evaluate the performance under various link error probabilities. Simulation results show that the exponential index substantially outperforms the state-of-the-art indexes. In particular, it is more resilient to link errors and achieves more performance advantages from index caching. The results also demonstrate its great flexibility in trading access latency with tuning time.

title: Exploring spatial correlation for link quality estimation in wireless sensor networks
abstract: The irregularity in quality of wireless communication links poses significant research challenges in wireless sensor network design. Dynamic network conditions and environmental factors make an online, self-adapted link quality estimation mechanism within sensor nodes a necessity for making routing decisions and improving network performance. In this paper, we present a weighted regression algorithm for efficient and accurate estimation of link quality in wireless sensor networks. This algorithm captures the spatial correlation in quality of links between a sensor node and its neighbor nodes, such that the quality of a link to a neighbor node can be estimated based on the quality of links to other nodes geographically close. We evaluate the proposed algorithm using a trace-based simulator which takes into account the variances of link quality over time and spatial locations. The experimental results show that the weighted regression algorithm is able to achieve more accurate estimates than WMEWMA, a state-of-the-art link quality estimator, at a much lower communication cost

title: Formalizing sensitivity in static analysis for intrusion detection
abstract: A key function of a host-based intrusion detection system is to monitor program execution. Models constructed using static analysis have the highly desirable feature that they do not produce false alarms; however, they may still miss attacks. Prior work has shown a trade-off between efficiency and precision. In particular, the more accurate models based upon pushdown automata (PDA) are very inefficient to operate due to non-determinism in stack activity. In this paper, we present techniques for determinizing PDA models. We first provide a formal analysis framework of PDA models and introduce the concepts of determinism and stack-determinism. We then present the VP-Static model, which achieves determinism by extracting information about stack activity of the program, and the Dyck model, which achieves stack-determinism by transforming the program and inserting code to expose program state. Our results show that in run-time monitoring, our models slow execution of our test programs by 1% to 135%. This shows that reasonable efficiency needs not be sacrificed for model precision. We also compare the two models and discover that deterministic PDA are more efficient, although stack-deterministic PDA require less memory.

title: A fully distributed spatial index for wireless data broadcast
abstract: To support location-based services in wireless data broadcast systems, a distributed spatial index (called DSI) is proposed in this paper. DSI is highly efficient because it has a linear yet fully distributed structure that naturally facilitates multiple replications of the index by sharing links in different search trees. Search algorithms for point queries, window queries, and kNN queries, based on DSI are presented. Empirical evaluation of DSI are conducted. Result shows that DSI significantly out-performs R-tree and Hilbert curve index, two state-of-the-art spatial indexing techniques for wireless data broadcast.

title: Word Sense Disambiguation with Semi-Supervised Learning
abstract: Current word sense disambiguation (WSD) systems based on supervised learning are still limited in that they do not work well for all words in a language. One of the main reasons is the lack of sufficient training data. In this paper, we investigate the use of unlabeled training data for WSD, in the framework of semi-supervised learning. Four semisupervised leaming algorithms are evaluated on 29 nouns of Senseval-2 (SE2) English lexical sample task and SE2 English all-words task. Empirical results show that unlabeled data can bring significant improvement in WSD accuracy.

title: Distributed scalable location data management system based on the GALIS architecture
abstract: A challenging task in the LBS system engineering is to implement a highly scalable system architecture which can manage moderate-size configurations handling thousands of moving items as well as upper-end configurations handling millions of moving items. The architecture named the GALIS is a cluster-based distributed computing system architecture that consists of multiple data processors, each dedicated to keeping records relevant to a different geographical zone and a different time zone. In this paper, we explain a prototype location data management system structuring major components of GALIS by employing the TMO programming scheme, including the execution engine middleware developed to support real-time distributed object programming and real-time distributed computing system design. We present how to generate realistic location sensing reports and how to process such location reports and location-related queries. Some experimental results showing performance factors regarding distributed query processing are also explained.

title: Mining in a data-flow environment: experience in network intrusion detection
abstract: We discuss the KDD process in “data-flow” environments, where unstructured and time dependent data can be processed into various levels of structured and semanticallyrich forms for analysis tasks. Using network intrusion detection as a concrete application example, we describe how to construct models that are both acczLrate in describing the underlying concepts, and efficient when used to analyze data in real-time. We present procedures for analyzing frequent patterns from lower level data and constructing appropriate features to formulate higher level data. The features generated from various levels of data have different computational costs (in time and space). We show that in order to minimize the time required in using the classification models in a real-time environment, we can exploit the “necessary conditions” associated with the lowcost features to determine whether some high-cost features need to be computed and the corresponding classification rules need to be checked. We have applied our tools to the problem of building network intrusion detection models. We report our experiments using the network data provided as part of the 1998 DARPA Intrusion Detection Evaluation program. We also discuss our experience in using the mined models in NFR, a real-time network intrusion detection system.