Search

Interval scales are assumed by several basic descriptive statistics, such as mean and variance, and by many statistical significance tests which are daily used in IR to compare systems. Unfortunately, so far, there has not been any systematic and formal study to discover the actual scale properties of IR measures. Therefore, in this paper, we develop a theory of Information Retrieval (IR) evaluation measures, based on the representational theory of measurements, to determine whether and when IR measures are interval scales. We found that common set-based retrieval measures—namely Precision, Recall, and F-measure—always are interval scales in the case of binary relevance while this happens also in the case of multi-graded relevance only when the relevance degrees themselves are on a ratio scale and we define a specific partial order among systems. In the case of rank-based retrieval measures—namely AP, gRBP, DCG, and ERR—only gRPB is an interval scale when we choose a specific value of the parameter $p$ and define a specific total order among systems while all the other IR measures are not interval scales. Besides the formal framework itself and the proof of the scale properties of several commonly used IR measures, the paper also defines some brand new set-based and rank-based IR evaluation measures which ensure to be interval scales. [ABSTRACT FROM AUTHOR]

It is not easy to obtain the right information from the Web for a particular Web user or a group of users due to the obstacle of automatically acquiring Web user profiles. The current techniques do not provide satisfactory structures for mining Web user profiles. This paper presents a novel approach for this problem. The objective of the approach is to automatically discover ontologies from data sets in order to build complete concept models for Web user information needs, It also proposes a method for capturing evolving patterns to refine discovered ontologies. In addition, the process of assessing relevance in ontology is established. This paper provides both theoretical and experimental evaluations for the approach. The experimental results show that all objectives we expect for the approach are achievable. [ABSTRACT FROM AUTHOR]

Data caching at mobile clients is an important technique for improving the performance of wireless data dissemination systems. However, variable data sizes, data updates, limited client resources, and frequent client disconnections make cache management a challenge. In this paper, we propose a gain-based cache replacement policy, Min-SAUD, for wireless data dissemination when cache consistency must be enforced before a cached item is used. Min-SAUD considers several factors that affect cache performance, namely, access probability, update frequency, data size, retrieval delay, and cache validation cost. This paper employs stretch as the major performance metric since it accounts for the data service time and, thus, is fair when items have different sizes. We prove that Min-SAUD achieves optimal stretch under some standard assumptions. Moreover, a series of simulation experiments have been conducted to thoroughly evaluate the performance of Min-SAUD under various system configurations. The simulation results show that, in most cases, the Min-SAUD replacement policy substantially outperforms two existing policies, namely, LRU and SAIU. [ABSTRACT FROM AUTHOR]

As the amount of information increases every day and the users normally formulate short and ambiguous queries, personalized search techniques are becoming almost a must. Using the information about the user stored in a user profile, these techniques retrieve results that are closer to the user preferences. On the other hand, the information is being stored more and more in an semi-structured way, and XML has emerged as a standard for representing and exchanging this type of data. XML search allows a higher retrieval effectiveness, due to its ability to retrieve and to show the user specific parts of the documents instead of the full document. In this paper we propose several personalization techniques in the context of XML retrieval. We try to combine the different approaches where personalization may be applied: query reformulation, re-ranking of results and retrieval model modification. The experimental results obtained from a user study using a parliamentary document collection support the validity of our approach. [ABSTRACT FROM AUTHOR]

Measuring similarity between objects is a fundamental task in domains such as data mining, information retrieval, and so on. Link-based similarity measures have attracted the attention of many researchers and have been widely applied in recent years. However, most previous works mainly focus on introducing new link-based measures, and seldom provide theoretical as well as experimental comparisons with other measures. Thus, selecting the suitable measure in different situations and applications is difficult. In this paper, a comprehensive analysis and critical comparison of various link-based similarity measures and algorithms are presented. Their strengths and weaknesses are discussed. Their actual runtime performances are also compared via experiments on benchmark data sets. Some novel and useful guidelines for users to choose the appropriate link-based measure for their applications are discovered. [ABSTRACT FROM PUBLISHER]

Social multimedia sharing and hosting websites, such as Flickr and Facebook, contain billions of user-submitted images. Popular Internet commerce websites such as Amazon.com are also furnished with tremendous amounts of product-related images. In addition, images in such social networks are also accompanied by annotations, comments, and other information, thus forming heterogeneous image-rich information networks. In this paper, we introduce the concept of (heterogeneous) image-rich information network and the problem of how to perform information retrieval and recommendation in such networks. We propose a fast algorithm heterogeneous minimum order k-SimRank (HMok-SimRank) to compute link-based similarity in weighted heterogeneous information networks. Then, we propose an algorithm Integrated Weighted Similarity Learning (IWSL) to account for both link-based and content-based similarities by considering the network structure and mutually reinforcing link similarity and feature weight learning. Both local and global feature learning methods are designed. Experimental results on Flickr and Amazon data sets show that our approach is significantly better than traditional methods in terms of both relevance and speed. A new product search and recommendation system for e-commerce has been implemented based on our algorithm. [ABSTRACT FROM AUTHOR]

Temporal databases facilitate the support of historical information by providing functions for indicating the intervals during which a tuple was applicable (along one or more temporal dimensions). Because data are never deleted, only superceded, temporal databases are inherently append-only, resulting, over time, in a large historical sequence of database states. Data vacuuming in temporal databases allows for this sequence to be shortened by strategically, and irrevocably, deleting obsolete data. Schema versioning allows users to maintain a history of database schemata without compromising the semantics of the data or the ability to view data through historical schemata. While the techniques required for data vacuuming in temporal databases have been relatively well covered, the associated area of vacuuming schemata has received less attention. This paper discusses this issue and proposes a mechanism that fits well with existing methods for data vacuuming and schema versioning. [ABSTRACT FROM AUTHOR]

Effective distance functions in high-dimensional data space, such as those used for clustering, nearest neighbor search, and indexing, are very important in solutions for many data mining problems. Recent research has shown that if the Pearson variation of the distance distribution converges to zero with increasing dimensionality, the distance function will become unstable (or meaningless) in high-dimensional space, even with the commonly used Lp, metric in the euclidean space. However, the necessary condition for unstability of a distance function, which is required for function design, remains unknown. In this paper, we shall prove that the following conditions are equivalent to unstability: 1) the Pearson variation of the distance distribution for any given query converges to 0 with increasing dimensionality, 2) the ratio of the distances of any two points to th query converges in probability to 1 with increasing dimensionality, and 3) the second moment coefficient converges to 1 with increasing dimensionality. With these results, we have the necessary and the sufficient conditions for unstability, whose negatives imply the sufficient and necessary conditions for stability. Based on these theoretical results, we employ some effective and valid indices for testing the stability of a distance function. In addition, this theoretical analysis inspires us that unstable phenomena are rooted in variation of the distance distribution. To demonstrate the theoretical results, we design a meaningful distance function, called the Shrinkage-Divergence Proximity (SDP), based on a given distance function. It is shown empirically that the SDP significantly outperforms other measures in terms of stability in high-dimensional data space, and is thus more suitable for distance-based applications. [ABSTRACT FROM AUTHOR]

The importance of query processing over uncertain data has recently arisen due to its wide usage in many real-world applications. In the context of uncertain databases, previous works have studied many query types such as nearest neighbor query, - range query, top-k query, skyline query, and similarity join. In this paper, we focus on another important query, namely, probabilistic group nearest neighbor (PGNN) query, in the uncertain database, which also has many applications. Specifically, given a set, Q, of query points, a PGNN query retrieves data objects that minimize the aggregate distance (e.g., sum, mm, and max) to query set Q. Due to the inherent uncertainty of data objects, previous techniques to answer group nearest neighbor (GNN) query cannot be directly applied to our PGNN problem. Motivated by this, we propose effective pruning methods, namely, spatial pruning and probabilistic pruning, to reduce the PGNN search space, which can be seamlessly integrated into our PGNN query procedure. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach, in terms of the wall clock time and the speed-up ratio against linear scan. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel algorithm for mining complete frequent itemsets. This algorithm is referred to as the TM (Transaction Mapping) algorithm from hereon. In this algorithm, transaction ids of each itemset are mapped and compressed to continuous transaction intervals in a different space and the counting of itemsets is performed by intersecting these interval lists in a depth-first order along the lexicographic tree. When the compression coefficient becomes smaller than the average number of comparisons for intervals intersection at a certain level, the algorithm switches to transaction id intersection. We have evaluated the algorithm against two popular frequent itemset mining algorithms, FP-growth and dEclat, using a variety of data sets with short and long frequent patterns. Experimental data show that the TM algorithm outperforms these two algorithms. [ABSTRACT FROM AUTHOR]

Repositories of multimedia objects having multiple types of attributes (e.g., image, text) are becoming increasingly common. A query on these attributes will typically request not just a set of objects, as in the traditional relational query model (filtering), but also a grade of match associated with each object, which indicates how well the object matches the selection condition (ranking). Furthermore, unlike in the relational model, users may just want the k top-ranked objects for their selection queries for a relatively small k. In addition to the differences in the query model, another peculiarity of multimedia repositories is that they may allow access to the attributes of each object only through indexes. In this paper, we investigate how to optimize the processing of top-k selection queries over multimedia repositories. The access characteristics of the repositories and the above query model lead to novel issues in query optimization. In particular, the choice of the indexes used to search the repository strongly influences the cost of processing the filtering condition. We define an execution space that is search-minimal, i.e., the set of indexes searched is minimal. Although the general problem of picking an optimal plan in the search-minimal execution space is NP-hard, we present an efficient algorithm that solves the problem optimally with respect to our cost model and execution space when the predicates in the query are independent. We also show that the problem of optimizing top-k selection queries can be viewed, in many cases, as that of evaluating more traditional selection conditions. Thus, both problems can be viewed together as an extended filtering problem to which techniques of query processing and optimization may be adapted. [ABSTRACT FROM AUTHOR]

With the growing demand for visual information of rich content, effective and efficient manipulations of large video databases are increasingly desired. Many investigations have been made on content-based video retrieval. However, despite the importance, video subsequence identification, which is to find the similar content to a short query clip from a long video sequence, has not been well addressed. This paper presents a graph transformation and matching approach to this problem, with extension to identify the occurrence of potentially different ordering or length due to content editing. With a novel batch query algorithm to retrieve similar frames, the mapping relationship between the query and database video is first represented by a bipartite graph. The densely matched parts along the long sequence are then extracted, followed by a filter-and-refine search strategy to prune some irrelevant subsequences. During the filtering stage, Maximum Size Matching is deployed for each subgraph constructed by the query and candidate subsequence to obtain a smaller set of candidates. During the refinement stage, Sub-Maximum Similarity Matching is devised to identify the subsequence with the highest aggregate score from all candidates, according to a robust video similarity model that incorporates visual content, temporal order, and frame alignment information. The performance studies conducted on a long video recording of 50 hours validate that our approach is promising in terms of both search accuracy and speed. [ABSTRACT FROM AUTHOR]

An XML publish/subscribe system needs to filter a large number of queries over XML streams. Most existing systems only consider filtering the simple XPath statements. In this paper, we focus on filtering of the more complex Generalized-Tree-Pattern (GTP) queries. Our filtering mechanism is based on a novel Tree-of-Path (TOP) encoding scheme, which compactly represents the path matches for the entire document. First, we show that the TOP encodings can be efficiently produced via shared bottom-up path matching. Second, with the aid of this TOP encoding, we can 1) achieve polynomial time and space complexity for postprocessing, 2) avoid redundant predicate evaluations, 3) allow an efficient duplicate-free and merge join-based algorithm for merging multiple encoded path matches, and 4) simplify the processing of GTP queries. Overall, our approach maximizes the sharing opportunity across queries by exploiting the suffix as well as prefix sharing. At the same time, our TOP encodings allow efficient postprocessing for GTP queries. Extensive performance studies show that GFilter not only achieves significantly better filtering performance than state-of-the-art algorithms but also is capable of efficiently filtering the more complex GTP queries. [ABSTRACT FROM AUTHOR]

A number of algorithms have been proposed for the discovery of temporal patterns. However, since the number of generated patterns can be large, selecting which patterns to analyze can be nontrivial. There is thus a need for algorithms and tools that can assist in the selection of discovered patterns so that subsequent analysis can be performed in an efficient and, ideally, interactive manner. In this paper, we propose a signature-based indexing method to optihiize the storage and retrieval of a large collection of relative temporal patterns. [ABSTRACT FROM AUTHOR]

Skyline arid top-k queries are two popular operations for preference retrieval. In practice, applications that require these operations usually provide numerous candidate attributes, whereas, depending on their interests, users may issue queries regarding different subsets of the dimensions. The existing algorithms are inadequate for subspace skyline/top-k search because they have at least one of the following defects: 1) They require scanning the entire database at least once, 2) they are optimized for one subspace but incur significant overhead for other subspaces, or 3) they demand expensive maintenance cost or space consumption. In this paper, we propose a technique SUBSKY, which settles both types of queries by using purely relational technologies. The core of SUBSKY is a transformation that converts multidimensional data to one-dimensional (1D) values. These values are indexed by a simple B-tree, which allows us to answer subspace queries by accessing a fraction of the database. SUBSKY entails low maintenance overhead, which equals the cost of updating a traditional B-tree. Extensive experiments with real data confirm that our technique outperforms alternative solutions significantly in both efficiency and scalability. [ABSTRACT FROM AUTHOR]

Streams are preferable over data stored in memory in contexts where data is too large or volatile, or a standard approach to data processing based on storing is too time or space consuming. Emerging applications such as publish-subscribe systems, data monitoring in sensor networks, financial and traffic monitoring, and routing of MPEG-7 call for querying streams. In many such applications, XML streams are arguably more appropriate than flat streams, for they convey (possibly unbounded) unranked ordered trees with labeled nodes. However, the flexibility enabled by XML streams in data modeling makes query evaluation different from traditional settings and challenging. This paper describes SPEX, a streamed and progressive evaluation of XML Path Language (XPath). SPEX compiles queries into networks of simple and independent transducers and processes XML streams with polynomial combined complexity. This makes SPEX especially suitable for implementation on devices with low memory and simple logic as used, for example, in mobile computing. [ABSTRACT FROM AUTHOR]

Acquisition of context poses unique challenges to mobile context-aware recommender systems. The limited resources in these systems make minimizing their context acquisition a practical need, and the uncertainty in the mobile environment makes missing and erroneous context inputs a major concern. In this paper, we propose an approach based on Bayesian networks (BNs) for building recommender systems that minimize context acquisition. Our learning approach iteratively trims the BN-based context model until it contains only the minimal set of context parameters that are important to a user. In addition, we show that a two-tiered context model can effectively capture the causal dependencies among context parameters, enabling a recommender system to compensate for missing and erroneous context inputs. We have validated our proposed techniques on a restaurant recommendation data set and a Web page recommendation data set. In both benchmark problems, the minimal sets of context can be reliably discovered for the specific users. Furthermore, the learned Bayesian network consistently outperforms the J4.8 decision tree in overcoming both missing and erroneous context inputs to generate significantly more accurate predictions. [ABSTRACT FROM AUTHOR]

With the rapid increase in both centralized video archives and distributed WWW video resources, content-based video retrieval is gaining its importance. To support such applications efficiently, content-based video indexing must be addressed. Typically, each video is represented by a sequence of frames. Due to the high dimensionality of frame representation and the large number of frames, video indexing introduces an additional degree of complexity. In this paper, we address the problem of content-based video indexing and propose an efficient solution, called the Ordered VA-File (OVA-File) based on the VA-file. OVA-File is a hierarchical structure and has two novel features: 1) partitioning the whole file into slices such that only a small number of slices are accessed and checked during κ Nearest Neighbor (kNN) search and 2) efficient handling of insertions of new vectors into the OVA-File, such that the average distance between the new vectors and those approximations near that position is minimized. To facilitate a search, we present an efficient approximate kNN algorithm named Ordered VA-LOW (OVA-LOW) based on the proposed OVA-File. OVA-LOW first chooses possible OVA-Slices by ranking the distances between their corresponding centers and the query vector, and then visits all approximations in the selected OVA-Slices to work out approximate kNN. The number of possible OVA-Slices is controlled by a user- defined parameter δ. By adjusting δ, OVA-LOW provides a trade-off between the query cost and the result quality. Query by video clip consisting of multiple frames is also discussed. Extensive experimental studies using real video data sets were conducted and the results showed that our methods can yield a significant speed-up over an existing VA-file-based method and iDistance with high query result quality. Furthermore, by incorporating temporal correlation of video content, our methods achieved much more efficient performance. [ABSTRACT FROM AUTHOR]

We refer ‘time-constrained serviced’ to those requests that have to be replied to within a certain client-expected time duration. If the answer cannot reach the client within this expected time, the value of the information may seriously degrade or even become useless. On-demand channels may not be able to handle all time-constrained services without degrading the performance. How to handle these services in broadcast channels becomes crucial to balance the load of wireless systems. In this paper, we study this problem and find the minimum number of broadcast channels required for such a task. Also, we propose solutions for this problem when the available channels are insufficient. Our performance result reveals that only a moderate number of channels is required to promote these time-constrained services. [ABSTRACT FROM AUTHOR]

Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level image features and high-level concepts. Given the error-prone nature of log data, we present a novel learning technique, named Soft Label Support Vector Machine, to tackle the noisy data problem. Extensive experiments are designed and conducted to evaluate the proposed algorithms based on the COREL image data set. The promising experimental results validate the effectiveness of our log-based relevance feedback scheme empirically. [ABSTRACT FROM AUTHOR]

A reverse nearest neighbor (RNN) query returns the data objects that have a query point as their nearest neighbor (NN). Although such queries have been studied quite extensively in Euclidean spaces, there is no previous work in the context of large graphs. In this paper, we provide a fundamental lemma, which can be used to prune the search space while traversing the graph in search for RNN. Based on it, we develop two RNN methods; an eager algorithm that attempts to prune network nodes as soon as they are visited and a lazy technique that prunes the search space when a data point is discovered. We study retrieval of an arbitrary number k of reverse nearest neighbors, investigate the benefits of materialization, cover several query types, and deal with cases where the queries and the data objects reside on nodes or edges of the graph. The proposed techniques are evaluated in various practical scenarios involving spatial maps, computer networks, and the DBLP coauthorship graph. [ABSTRACT FROM AUTHOR]

As databases increasingly integrate different types of information such as multimedia, spatial, time-series, and scientific data, it becomes necessary to support efficient retrieval of multidimensional data. Both the dimensionality and the amount of data that needs to be processed are increasing rapidly. Reducing the dimension of the feature vectors to enhance the performance of the underlying technique is a popular solution to the infamous curse of dimensionality. We expect the techniques to have good quality of distance measures when the similarity distance between two feature vectors is approximated by some notion of distance between two lower-dimensional transformed vectors. Thus, it is desirable to develop techniques resulting in accurate approximations to the original similarity distance. In this paper, we investigate dimensionality reduction techniques that directly target minimizing the errors made in the approximations. In particular, we develop dynamic techniques for efficient and accurate approximation of similarity evaluations between high-dimensional vectors based on inner-product approximations. Inner-product, by itself, is used as a distance measure in a wide area of applications such as document databases. A first order approximation to the inner-product is obtained from the Cauchy-Schwarz inequality. We extend this idea to higher order power symmetric functions of the multidimensional points. We show how to compute fixed coefficients that work as universal weights based on the moments of the probability density function of the data set. We also develop a dynamic model to compute the universal coefficients for data sets whose distribution is not known. Our experiments on synthetic and real data sets show that the similarity between two objects in high-dimensional space can be accurately approximated by a significantly lower-dimensional representation. [ABSTRACT FROM AUTHOR]

Keyword search is an effective paradigm for information discovery and has been introduced recently to query XML documents. In this paper, we address the problem of returning clustered results for keyword search on XML documents. We first propose a novel semantics for answers to an XML keyword query. The core of the semantics is the conceptually related relationship between keyword matches, which is based on the conceptual relationship between nodes in XML trees. Then, we propose a new clustering methodology for XML search results, which clusters results according to the way they match the given query. Two approaches to implement the methodology are discussed. The first approach is a conventional one which does clustering after search results are retrieved; the second one clusters search results actively, which has characteristics of clustering on the fly. The generated clusters are then organized into a cluster hierarchy with different granularities to enable users locate the results of interest easily and precisely. Experimental results demonstrate the meaningfulness of the proposed semantics as well as the efficiency of the proposed methods. [ABSTRACT FROM PUBLISHER]

Existing studies on keyword search over relational databases usually find Steiner trees composed of connected database tuples as answers. They on-the-fly identify Steiner trees by discovering rich structural relationships between database tuples, and neglect the fact that such structural relationships can be precomputed and indexed. Recently, tuple units are proposed to improve search efficiency by indexing structural relationships, and existing methods identify a single tuple unit to answer keyword queries. However, in many cases, multiple tuple units should be integrated to answer a keyword query. Thus, these methods will involve false negatives. To address this problem, in this paper, we study how to integrate multiple related tuple units to effectively answer keyword queries. To achieve a high performance, we devise two novel indexes, single-keyword-based structure-aware index and keyword-pair-based structure-aware index, and incorporate structural relationships between different tuple units into the indexes. We use the indexes to efficiently identify the answers of integrated tuple units. We develop new ranking techniques and algorithms to progressively find the top-k answers. We have implemented our method in real database systems, and the experimental results show that our approach achieves high search efficiency and result quality, and outperforms state-of-the-art methods significantly. [ABSTRACT FROM PUBLISHER]

With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly. In this paper, we study the effectiveness and the efficiency issues of answering top-k keyword query in relational database systems. We propose a new ranking formula by adapting existing IR techniques based on a natural notion of virtual document. We also propose several efficient query processing methods for the new ranking method. We have conducted extensive experiments on large-scale real databases using two popular RDBMSs. The experimental results demonstrate significant improvement to the alternative approaches in terms of retrieval effectiveness and efficiency. [ABSTRACT FROM PUBLISHER]

Given an effectiveness metric $M(\cdot)$ , two ordered document rankings $X_1$ and $X_2$ generated by a score-based information retrieval activity, and relevance labels in regard to some subset (possibly empty) of the documents appearing in the two rankings, Tan and Clarke's Maximized Effectiveness Distance (MED) computes the greatest difference in metric score that can be achieved that is consistent with all provided information, crystallized via a set of relevance assignments to the unlabeled documents such that $|M(X_1)-M(X_2)|$ is maximized. The closer the maximized effectiveness distance is to zero, the more similar $X_1$ and $X_2$ can be considered to be from the point of view of the metric $M(\cdot)$ . Here, we consider issues that arise when Tan and Clarke's definitions are applied to recall-based metrics, notably normalized discounted cumulative gain (NDCG), and average precision (AP). In particular, we show that MED can be applied to NDCG without requiring an a priori assumption in regard to the total number of relevant documents; we also show that making such an assumption leads to different outcomes for both NDCG and average precision (AP) compared to when no such assumption is made. [ABSTRACT FROM PUBLISHER]

A search-as-you-type system computes answers on-the-fly as a user types in a keyword query character by character. We study how to support search-as-you-type on data residing in a relational DBMS. We focus on how to support this type of search using the native database language, SQL. A main challenge is how to leverage existing database functionalities to meet the high-performance requirement to achieve an interactive speed. We study how to use auxiliary indexes stored as tables to increase search performance. We present solutions for both single-keyword queries and multikeyword queries, and develop novel techniques for fuzzy search using SQL by allowing mismatches between query keywords and answers. We present techniques to answer first-N queries and discuss how to support updates efficiently. Experiments on large, real data sets show that our techniques enable DBMS systems on a commodity computer to support search-as-you-type on tables with millions of records. [ABSTRACT FROM PUBLISHER]

Multidimensional point indexing plays a critical role in a variety of data-centric applications, including image retrieval, sequence matching, and moving object database search. A common choice of indexing method for these applications is often the "ubiquitous" R*-tree. Choosing the right indexing method requires careful consideration of various factors such as query operations and index construction methods. In this work, we present an experimental study comparing the R*-tree and Quadtree using various criteria including the query operations and index construction methods. Although a variety of query operations can be performed using these index structures, previous work has largely focused only on the range search operation. We go beyond this previous work and compare the performance of these index structures using k-nearest neighbor (kNN) and distance join queries. In addition, we also consider the impact of index construction methods in evaluating these index structures. Our study sheds light on how the choice of the underlying index structure affects the performance of different query operations, and shows that the method used for constructing the index and the dynamic nature of the data set has a dramatic impact on the performance of these index structures. [ABSTRACT FROM AUTHOR]

One of the fundamental problems in Content-Based Image Retrieval (CBIR) has been the gap between low-level visual features and high-level semantic concepts. To narrow down this gap, relevance feedback is introduced into image retrieval. With the user-provided information, a classifier can be learned to distinguish between positive and negative examples. However, in real-world applications, the number of user feedbacks is usually too small compared to the dimensionality of the image space. In order to cope with the high dimensionality, we propose a novel semisupervised method for dimensionality reduction called Maximum Margin Projection (MMP). MMP aims at maximizing the margin between positive and negative examples at each local neighborhood. Different from traditional dimensionality reduction algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which effectively see only the global euclidean structure, MMP is designed for discovering the local manifold structure. Therefore, MMP is likely to be more suitable for image retrieval, where nearest neighbor search is usually involved. After projecting the images into a lower dimensional subspace, the relevant images get closer to the query image; thus, the retrieval performance can be enhanced. The experimental results on Corel image database demonstrate the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

We address wildcard search in structured peer-to-peer (P2P) networks, which, to our knowledge, has not yet been explored in the literature. We begin by presenting an approach based on some well-known techniques in information retrieval (lR) and discuss why it is not appropriate in a distributed environment. We then present a simple and novel technique to index objects for wildcard search in a fully decentralized manner, along with some search strategies to retrieve objects. Our index scheme, as opposed to a traditional IR approach, can achieve quite balanced loads, avoid hop spots and single point of failure, reduce storage and maintenance costs, and offer some ranking mechanisms for matching objects. We use the compact disc (CD) records collected in FreeDB (http://freedb.org) as the experimental data set to evaluate our scheme. The results confirm that our index scheme is very effective in balancing the load. Moreover, search efficiency depends on the information given in a query: the more the information, the higher the performance. [ABSTRACT FROM AUTHOR]

Current document retrieval methods use a vector space similarity measure to give scores of relevance to documents when related to a specific query. The central problem with these methods is that they neglect any spatial information within the documents in question. We present a new method, called Fourier Domain Scoring (FDS), which takes advantage of this spatial information, via the Fourier transform, to give a more accurate ordering of relevance to a document set. We show that FDS gives an improvement in precision over the vector space similarity measures for the common case of Web like queries, and it gives similar results to the vector space measures for longer queries. [ABSTRACT FROM AUTHOR]