Web data retrieval: solving spatial range queries using k-nearest neighbor searches

As Geographic Information Systems (GIS) technologies have evolved, more and more GIS applications and geospatial data are available on the web. Spatial objects in a given query range can be retrieved using spatial range query − one of the most widely used query types in GIS and spatial databases. However, it can be challenging to retrieve these data from various web applications where access to the data is only possible through restrictive web interfaces that support certain types of queries. A typical scenario is the existence of numerous business web sites that provide their branch locations through a limited “nearest location” web interface. For example, a chain restaurant’s web site such as McDonalds can be queried to find some of the closest locations of its branches to the user’s home address. However, even though the site has the location data of all restaurants in, for example, the state of California, it is difficult to retrieve the entire data set efficiently due to its restrictive web interface. Considering that k-Nearest Neighbor (k-NN) search is one of the most popular web interfaces in accessing spatial data on the web, this paper investigates the problem of retrieving geospatial data from the web for a given spatial range query using only k-NN searches. Based on the classification of k-NN interfaces on the web, we propose a set of range query algorithms to completely cover the rectangular shape of the query range (completeness) while minimizing the number of k-NN searches as possible (efficiency). We evaluated the efficiency of the proposed algorithms through statistical analysis and empirical experiments using both synthetic and real data sets.

Evaluation of probabilistic queries over imprecise data in constantly-evolving environments

Sensors are often employed to monitor continuously changing entities like locations of moving objects and temperature. The sensor readings are reported to a database system, and are subsequently used to answer queries. Due to continuous changes in these values and limited resources (e.g., network bandwidth and battery power), the database may not be able to keep track of the actual values of the entities. Queries that use these old values may produce incorrect answers. However, if the degree of uncertainty between the actual data value and the database value is limited, one can place more confidence in the answers to the queries. More generally, query answers can be augmented with probabilistic guarantees of the validity of the answers. In this paper, we study probabilistic query evaluation based on uncertain data. A classification of queries is made based upon the nature of the result set. For each class, we develop algorithms for computing probabilistic answers, and provide efficient indexing and numeric solutions. We address the important issue of measuring the quality of the answers to these queries, and provide algorithms for efficiently pulling data from relevant sensors or moving objects in order to improve the quality of the executing queries. Extensive experiments are performed to examine the effectiveness of several data update policies.     

Adaptive optimization for multiple continuous queries

Because it operates under a strict time constraint, query processing for data streams should be continuous and rapid. To guarantee this constraint, most previous researches optimize the evaluation order of multiple join operations in a set of continuous queries using a greedy optimization strategy so that the order is re-optimized dynamically in run-time due to the time-varying characteristics of data streams. However, this method often results in a sub-optimal plan because the greedy strategy traces only the first promising plan. This paper proposes a new multiple query optimization approach, Adaptive Sharing-based Extended Greedy Optimization Approach (A-SEGO), that traces multiple promising partial plans simultaneously. A-SEGO presents a novel method for sharing the results of common sub-expressions in a set of queries cost-effectively. The number of partial plans can be flexibly controlled according to the query processing workload. In addition, to avoid invoking the optimization process too frequently, optimization is performed only when the current execution plan is relatively no longer efficient. A series of experiments are comparatively analyzed to evaluate the performance of the proposed method in various stream environments.     

Selectivity estimation of range queries based on data density approximation via cosine series

Selectivity estimation is an integral part of query optimization. In this paper, we propose to approximate data density functions of relations by cosine series and use the approximations to estimate selectivities of range queries. We lay down the foundation for applying cosine series to range query size estimation and compare it with some notable approaches, such as the wavelets, DCT, kernel-spline, sketch, and Legendre polynomials. Experimental results have shown that our approach is simple to construct, easy to update, and fast to estimate. It also yields accurate estimates, especially in multi-dimensional cases.     

Similar sequence matching supporting variable-length and variable-tolerance continuous queries on time-series data stream

We propose a new similar sequence matching method that efficiently supports variable-length and variable-tolerance continuous query sequences on time-series data stream. Earlier methods do not support variable lengths or variable tolerances adequately for continuous query sequences if there are too many query sequences registered to handle in main memory. To support variable-length query sequences, we use the window construction mechanism that divides long sequences into smaller windows for indexing and searching the sequences. To support variable-tolerance query sequences, we present a new notion of intervaled sequences whose individual entries are an interval of real numbers rather than a real number itself. We also propose a new similar sequence matching method based on these notions, and then, formally prove correctness of the method. In addition, we show that our method has the prematching characteristic, which finds future candidates of similar sequences in advance. Experimental results show that our method outperforms the naive one by 2.6-102.1 times and the existing methods in the literature by 1.4-9.8 times over the entire ranges of parameters tested when the query selectivities are low (<32%), which are practically useful in large database applications.     

Context-sensitive queries for image retrieval in digital libraries

In this paper we show how to achieve a more effective Query By Example processing, by using active mechanisms of biological vision, such as saccadic eye movements and fixations. In particular, we discuss the way to generate two fixation sequences from a query image I _q and a test image I _t of the data set, respectively, and how to compare the two sequences in order to compute a similarity measure between the two images. Meanwhile, we show how the approach can be used to discover and represent the hidden semantic associations among images, in terms of categories, which in turn drive the query process.     

Exploiting syntactic analysis of queries for information retrieval

Up to now, the results of applying sophisticated NL techniques to information retrieval (IR) have been mostly disappointing. Our research aims at investigating in detail the role of syntactic analysis in IR and at finding answers to the question why it works better for some queries and worse for others. The final goal is a hybrid algorithm that selectively applies syntactic analysis to certain classes of queries while relying on standard statistical techniques otherwise.     

An indexing scheme for energy-efficient processing of content-based retrieval queries on a wireless data stream

Wireless data broadcasting is a popular data delivery approach in mobile computing environments, where the broadcasting servers usually adopt indexing schemes for mobile clients to energy-efficiently access data on a wireless broadcast stream. However, conventional indexing schemes use primary key attribute values to construct tree structures. Therefore, these schemes do not support content-based retrieval queries such as partial-match queries and range-queries. This paper proposes an indexing method that supports content-based retrieval queries on a wireless data stream. The method uses a tree-structured index, called B2V-Tree, which is composed of bit-vectors that are generated from data records through multi-attribute hashing. Through analysis and experiments, the effectiveness of the proposed method is shown.     

MINDEX: An efficient index structure for salient-object-based queries in video databases

Several salient-object-based data models have been proposed to model video data. However, none of them addresses the development of an index structure to efficiently handle salient-object-based queries. There are several indexing schemes that have been proposed for spatiotemporal relationships among objects, and they are used to optimize timestamp and interval queries, which are rarely used in video databases. Moreover, these index structures are designed without consideration of the granularity levels of constraints on salient objects and the characteristics of video data. In this paper, we propose a multilevel index structure (MINDEX) to efficiently handle the salient-object-based queries with different levels of constraints. We present experimental results showing the performance of different methods of MINDEX construction.     

Integrating document and data retrieval based on XML

For querying structured and semistructured data, data retrieval and document retrieval are two valuable and complementary techniques that have not yet been fully integrated. In this paper, we introduce integrated information retrieval (IIR), an XML-based retrieval approach that closes this gap. We introduce the syntax and semantics of an extension of the XQuery language called XQuery/IR. The extended language realizes IIR and thereby allows users to formulate new kinds of queries by nesting ranked document retrieval and precise data retrieval queries. Furthermore, we detail index structures and efficient query processing approaches for implementing XQuery/IR. Based on a new identification scheme for nodes in node-labeled tree structures, the extended index structures require only a fraction of the space of comparable index structures that only support data retrieval.     

Maintaining consistent results of continuous queries under diverse window specifications

Continuous queries applied over nonterminating data streams usually specify windows in order to obtain an evolving–yet restricted–set of tuples and thus provide timely and incremental results. Although sliding windows get frequently employed in many user requests, additional types like partitioned or landmark windows are also available in stream processing engines. In this paper, we set out to study the existence of monotonic-related semantics for a rich set of windowing constructs in order to facilitate a more efficient maintenance of their changing contents. After laying out a formal foundation for expressing windowed queries, we investigate update patterns observed in most common window variants as well as their impact on adaptations of typical operators (like windowed join, union or aggregation), thus offering more insight towards design and implementation of stream processing mechanisms. Furthermore, we identify syntactic equivalences in algebraic expressions involving windows, to the potential benefit of query optimizations. Finally, this framework is validated for several windowed operations against streaming datasets with simulations at diverse arrival rates and window specifications, providing concrete evidence of its significance.     

Nearest and reverse nearest neighbor queries for moving objects

With the continued proliferation of wireless communications and advances in positioning technologies, algorithms for efficiently answering queries about large populations of moving objects are gaining interest. This paper proposes algorithms for k nearest and reverse k nearest neighbor queries on the current and anticipated future positions of points moving continuously in the plane. The former type of query returns k objects nearest to a query object for each time point during a time interval, while the latter returns the objects that have a specified query object as one of their k closest neighbors, again for each time point during a time interval. In addition, algorithms for so-called persistent and continuous variants of these queries are provided. The algorithms are based on the indexing of object positions represented as linear functions of time. The results of empirical performance experiments are reported.     

Answering content and structure-based queries on XML documents using relevance propagation

As XML documents contain both content and structure information, taking advantage of the document structure in the retrieval process can lead to better identify relevant information units. In this paper, we describe an information retrieval (IR) approach dealing with queries composed of content and structure conditions. The XFIRM model we propose is designed to be as flexible as possible to process such queries. It is based on a complete query language, derived from XPath and on a relevance values propagation method. This paper aims at evaluating functions used in the propagation process, and particularly the use of distance between nodes as a parameter. The proposed method is evaluated, thanks to the INEX evaluation initiative. Results show a relative high precision of our proposal.     

Determinacy and query rewriting for conjunctive queries and views

Answering queries using views is the problem which examines how to derive the answers to a query when we only have the answers to a set of views. Constructing rewritings is a widely studied technique to derive those answers. In this paper we consider the problem of the existence of rewritings in the case where the answers to the views uniquely determine the answers to the query. Specifically, we say that a view set Vdetermines a query Q if for any two databases D₁,D₂ it holds: V(D₁)=V(D₂) implies Q(D₁)=Q(D₂). We consider the case where query and views are defined by conjunctive queries and investigate the question: If a view set V determines a query Q, is there an equivalent rewriting of Q using V? We present here interesting cases where there are such rewritings in the language of conjunctive queries. Interestingly, we identify a class of conjunctive queries, CQ_path, for which a view set can produce equivalent rewritings for “almost all” queries which are determined by this view set. We introduce a problem which relates determinacy to query equivalence. We show that there are cases where restricted results can carry over to broader classes of queries.     

The complexity of weighted counting for acyclic conjunctive queries

This paper is a study of weighted counting of the solutions of acyclic conjunctive queries (ACQ). The unweighted quantifier free version of this problem is known to be tractable (for combined complexity), but it is also known that introducing even a single quantified variable makes it #P$\mathrm{\#}\mathsf{P}$-hard. We first show that weighted counting for quantifier free ACQ is still tractable and that even minimalistic extensions of the problem lead to hard cases. We then introduce a new parameter for quantified queries that permits to isolate a large island of tractability. We show that, up to a standard assumption from parameterized complexity, this parameter fully characterizes tractable subclasses for counting weighted solutions for ACQs. Thus we completely determine the tractability frontier for weighted counting for ACQ.