Trio is a new database system that manages not only data, butalsotheaccuracy and lineage of the data. Inexact (uncertain, probabilistic, fuzzy, approximate, incomplete, and imprecise!) databases have been proposed in the past, and the lineage problem also has been studied. The goals of the Trio project are to combine and distill previous work into a simple and usable model, design a query language as an understandable extension to SQL, and most importantly build a working system—a system that augments conventional data management with both accuracy and lineage as an integral part of the data. This paper provides numerous motivating applications for Trio and lays out preliminary plans for the data model, query language, and prototype system.

Abstract. We study the problem of answering spatial queries in databases where objects exist with some uncertainty and they are associated with an existential probability. The goal of a thresholding probabilistic spatial query is to retrieve the objects that qualify the spatial predicates with probability that exceeds a threshold. Accordingly, a ranking probabilistic spatial query selects the objects with the highest probabilities to qualify the spatial predicates. We propose adaptations of spatial access methods and search algorithms for probabilistic versions of range queries and nearest neighbors and conduct an extensive experimental study, which evaluates the effectiveness of proposed solutions. 1

Abstract. An infrastructure is emerging that enables the positioning of populations of on-line, mobile service users. In step with this, research in the management of moving objects has attracted substantial attention. In particular, quite a few proposals now exist for the indexing of moving objects, and more are underway. As a result, there is an increasing need for an independent benchmark for spatio-temporal indexes. This paper characterizes the spatio-temporal indexing problem and proposes a benchmark for the performance evaluation and comparison of spatio-temporal indexes. Notably, the benchmark takes into account that the available positions of the moving objects are inaccurate, an aspect largely ignored in previous indexing research. The concepts of data and query enlargement are introduced for addressing inaccuracy. As proof of concepts of the benchmark, the paper covers the application of the benchmark to three spatio-temporal indexes—the TPR-, TPR*-, and B x-trees. Representative experimental results and consequent guidelines for the usage of these indexes are reported. 1

Abstract—We study the problem of answering spatial queries in databases where objects exist with some uncertainty and they are associated with an existential probability. The goal of a thresholding probabilistic spatial query is to retrieve the objects that qualify the spatial predicates with probability that exceeds a threshold. Accordingly, a ranking probabilistic spatial query selects the objects with the highest probabilities to qualify the spatial predicates. We propose adaptations of spatial access methods and search algorithms for probabilistic versions of range queries, nearest neighbors, spatial skylines, and reverse nearest neighbors and conduct an extensive experimental study, which evaluates the effectiveness of proposed solutions. Index Terms—H.2.4.h Query processing, H.2.4.k Spatial databases 1

Sensor networks consist of battery-powered wireless devices that are required to operate unattended for long periods of time. Thus, reducing energy drain is of utmost importance when designing algorithms and applications for such networks. Aggregate queries are often used by monitoring applications to assess the status of the network and detect abnormal behavior. Since radio transmission often constitutes the biggest factor of energy drain in a node, in this paper we propose novel algorithms for the evaluation of bandwidth-constrained queries over sensor networks. The goal of our techniques is, given a target bandwidth utilization factor, to program the sensor nodes in a way that seeks to maximize the accuracy of the produced query results at the monitoring node, while always providing strong error guarantees to the monitoring application. This is a distinct difference of our framework from previous techniques that only provide probabilistic guarantees on the accuracy of the query result. Our algorithms are equally applicable when the nodes have ample power resources, but bandwidth consumption needs to be minimized, for instance in densely distributed networks, to ensure proper operation of the nodes. Our experiments with real sensor data show that bandwidth-constrained queries can substantially reduce the number of messages in the network while providing very tight error bounds on the query result.

Distributed sensor applications emerge as a promising solution to be utilized for complex business scenarios. However, the development and deployment of these applications remains a complex challenge. In this survey we present a two-dimensional classification of middleware technologies needed to realize these complex enduser business scenarios. Subsequently, we discuss currently existing middleware approaches and place them in the classification framework we developed. Finally, based on this classification overview, we identify two issues for which middleware support leaps behind: cross-layer integration and end-to-end integration.

A lot of efforts have been devoted to improving energy efficiency for wireless sensor networks by exploring distributed data storage and in-network query processing techniques. In this paper, we propose a generic two-tier data storage strategy for answering precision-constrained approximate queries in a sensor network. The basic idea is to keep two versions of data in the network. A high-precision version is kept at the sensor node that captures the data while a low-precision version is maintained at the base station. We develop query processing and node refreshment strategies for various types of approximate queries under the two-tier storage. Our extensive experiments show that the two-tier storage strategy outperforms the basic centralized storage scheme by an order of magnitude in terms of network lifetime under various system configurations. 1

We consider the problem of evaluating continuous selection queries over sensor-generated values in the presence of faults. Small sensors are fragile, have finite energy and memory, and communicate over a lossy medium; hence, tuples produced by them may not reach the querying node, resulting in an incomplete and ambiguous answer, as any of the non-reporting sensors may have produced a tuple which was lost. We develop a protocol, FAult Tolerant Evaluation of Continuous Selection Queries (FATE-CSQ), which guarantees a user-requested level of quality in an efficient manner. When many faults occur, this may not be achievable; in that case, we aim for the best possible answer, under the query’s time constraints. FATE-CSQ is designed to be resilient to different kinds of failures. Our design decisions are based on an analytical model of different fault tolerance strategies based on feedback and retransmission. Additionally, we demonstrate the good performance of FATE-CSQ compared to competing protocols with realistic simulation parameters and under a variety of conditions. 1

This paper tackles a major privacy concern in current location-based services where users have to continuously report their locations to the database server in order to obtain the service. For example, a user asking about the nearest gas station has to report her exact location. With untrusted servers, reporting the location information may lead to several privacy threats. In this paper, we present Casper 1; a new framework in which mobile and stationary users can entertain location-based services without revealing their location information. Casper consists of two main components, the location anonymizer and the privacy-aware query processor. The location anonymizer blurs the users ’ exact location information into cloaked spatial regions based on userspecified privacy requirements. The privacy-aware query processor is embedded inside the location-based database server in order to deal with the cloaked spatial areas rather than the exact location information. Experimental results show that Casper achieves high quality location-based services while providing anonymity for both data and queries. 1.

The Analytical Mediator System (AMS) is a fully functional experimental prototype system for importing, cleansing, transforming and integrating, and analyzing heterogeneous data. The AMS provides a straightforward, SQL-like model for managing the entire data management lifecycle. Other critical features of the AMS include: its facility for constructing a domain model suitable for representing the combined semantics of an integrated data set, its easy-to-use facility for reducing the dimensionality of a large data set, its ability to interface with standard data mining tools, its simple GUI, and the easy-to-understand reports it generates.