The generation of voluminous scientific data poses significant challenges for efficient storage, transfer, and analysis. Recently, error-bounded lossy compression methods emerged due to their ability to achieve high compression ratios while controlling data distortion. However, they often overlook the inherent spatial and temporal correlations within scientific data, thus missing opportunities for higher compression. In this paper we propose GRAPHCOMP, a novel graph-based method for error-bounded lossy compression of scientific data. We perform irregular segmentation of the original grid data and generate a graph representation that preserves the spatial and temporal correlations. Inspired by Graph Neural Networks (GNNs), we then propose a temporal graph autoencoder to learn latent representations that significantly reduce the size of the graph, effectively compressing the original data. Decompression reverses the process and utilizes the learnt graph model together with the latent representation to reconstruct an approximation of the original data. The decompressed data are guaranteed to satisfy a user-defined point-wise error bound. We compare our method against the state-of-the-art error-bounded lossy methods (i.e., HPEZ, SZ3.1, SPERR, and ZFP) on large-scale real and synthetic data. GRAPHCOMP consistently achieves the highest compression ratio across most datasets, outperforming the second-best method by margins ranging from 22% to 50%.

How can we adapt the composition of a post over a series of rounds to make it more appealing in a social network? Techniques that progressively learn how to make a fixed post more influential over rounds have been studied in the context of the Influence Maximization (IM) problem, which seeks a set of seed users that maximize a post’s influence. However, there is no work on progressively learning how a post’s features affect its influence. In this article, we propose and study the problem of Adaptive Content-Aware Influence Maximization (ACAIM), which calls to find k features to form a post in each round so as to maximize the cumulative influence of those posts over all rounds. We solve ACAIM by applying, for the first time, an Online Learning to Rank (OLR) framework for IM purposes. We introduce the CATRID propagation model , which expresses how posts disseminate in a social network using click probabilities and post visibility criteria and develop a simulator that runs CATRID via a training-testing scheme based on real posts of the VK social network, so as to realistically represent the learning environment. We deploy three learners that solve ACAIM in an online (real-time) manner. We experimentally prove the practical suitability of our solutions via exhaustive experiments on multiple brands (operating as different case studies ) and several VK datasets; the best learner is evaluated on 45 separate case studies yielding convincing results.

Analyzing time series data is crucial for many applications. In particular, subsequence search refers to finding subsequences within an input time series T that are similar to a query sequence Q. Existing subsequence search approaches typically employ Euclidean distance or Dynamic Time Warping as similarity measures and address range queries. In this paper, we focus on Chebyshev distance, which is the largest difference between each individual pair of points across the entire length of two compared subsequences. We call such similar pairstwins. We first show how existing time series indices can be extended to perform twin subsequence search. Then, we introduce TS-Index, a novel index tailored to the computation of twin subsequence search queries. Moreover, given that specifying a distance threshold is often not straightforward, we show how TS-Index can also be used to evaluate kNN queries. Our extensive experimental evaluation compares these approaches using real time series datasets. The results demonstrate that TS-Index can retrieve twin subsequences faster than all other methods under various conditions.

How can we recommend content for a brand agent to use over a series of rounds so as to gain new subscribers to its social network page? The Influence Maximization (IM) problem seeks a set of~k users, and its content-aware variants seek a set of~k post features, that achieve, in both cases, an objective of expected influence in a social network. However, apart from raw influence, it is also relevant to study gain in subscribers, as long-term success rests on the subscribers of a brand page; classic IM may select~k users from the subscriber set, and content-aware IM starts the post's propagation from that subscriber set. In this paper, we propose a novel content recommendation policy to a brand agent for Gaining Subscribers by Messaging (GSM) over many rounds. In each round, the brand agent messages a fixed number of social network users and invites them to visit the brand page aiming to gain their subscription, while its most recently published content consists of features that intensely attract the preferences of the invited users. To solve GSM, we find, in each round, which content features to publish and which users to notify aiming to maximize the cumulative subscription gain over all rounds. We deploy three GSM solvers, named \sR, \sSC, and \sSU, and we experimentally evaluate their performance based on VKontakte (VK) posts by considering different user sets and feature sets. Our experimental results show that \sSU provides the best solution, as it is significantly more efficient than \sSC with a minor loss of efficacy and clearly more efficacious than \sR with competitive efficiency.

Unsupervised representation learning tends to produce generic and reusable latent representations. However, these representations can often miss high-level features or semantic information, since they only observe the implicit properties of the dataset. On the other hand, supervised learning frameworks learn task-oriented latent representations that may not generalise in other tasks or domains. In this article, we introduce evidence transfer, a deep learning method that incorporates the outcomes of external tasks in the unsupervised learning process of an autoencoder. External task outcomes also referred to as categorical evidence, are represented by categorical variables, and are either directly or indirectly related to the primary dataset—in the most straightforward case they are the outcome of another task on the same dataset. Evidence transfer allows the manipulation of generic latent representations in order to include domain or task-specific knowledge that will aid their effectiveness in downstream tasks. Evidence transfer is robust against evidence of low quality and effective when introduced with related, corresponding, or meaningful evidence.

An important task on land registration processes is to be able to determine the prevalent data provenance for a finalized polygon that represents a cadastral parcel, since the finalized polygon is derived by the examination of a set of initial polygons, drawn from several individual registers (databases). These registers might contain different, partially similar or conflicting information regarding the ownership, usage and polygon geometry of a cadastral parcel. In such cases, the cadastration expert either select one of of the initial geometries, or (in cases none of the initial accurately represents the finalized land parcel) creates a new geometry. Maintaining this provenance information is of high importance for further cadastration and validation/quality assessment processes; however, due to the gradual and long lasting nature of cadastration procedures, this information is absent from large parts of cadastral databases. In this paper, we present an approach for effectively classifying such land parcel polygons with respect to their provenance information. We propose a method that can produce highly accurate provenance recommendations based only on attributes derived from the geometry of a land parcel. In particular, we implement a set of spatial training features, capturing polygon properties and relations. These features are fed into several classification algorithms and are evaluated on a proprietary dataset of a cadastration company.

Investigating whether two different classifications of a population are associated, is an interesting problem in many scientific fields. For this reason, various statistical tests to reveal this type of associations have been developed, with the most popular of them being Fisher’s exact test. However it has lately been shown that in some cases this test fails to produce accurate results. An alternative approach, known as randomization tests, was introduced to alleviate this issue, however, such tests are computationally intensive. In this paper, we introduce two novel indexing approaches that exploit frequently occurring patterns in classifications to avoid performing redundant computations during the analysis. We conduct a comprehensive set of experiments using real datasets and application scenarios to show that our approaches always outperform the state-of-the-art, with one approach being faster by an order of magnitude.

Geolocated time series, i.e., time series associated with certain locations, abound in many modern applications. In this paper, we consider hybrid queries for retrieving geolocated time series based on filters that combine spatial distance and time series similarity. For the latter, unlike existing work, we allow filtering based on local similarity, which is computed based on subsequences rather than the entire length of each series, thus allowing the discovery of more fine-grained trends and patterns. To efficiently support such queries, we first leverage the state-of-the-art BTSR-tree index, which utilizes bounds over both the locations and the shapes of time series to prune the search space. Moreover, we propose optimizations that check at specific timestamps to identify candidate time series that may exceed the required local similarity threshold. To further increase pruning power, we introduce the SBTSR-tree index, an extension to BTSR-tree, which additionally segments the time series temporally, allowing the construction of tighter bounds. Our experimental results on several real-world datasets demonstrate that SBTSR-tree can provide answers much faster for all examined query types.

Clustering algorithms are used to find groups of similar items in a dataset. Automatic clustering algorithms achieve this task without requiring users to input critical parameters. A recent automatic clustering methodology uses Hartigan's dip test to detect significant peaks in the distribution of a dataset. This test can detect peaks in the distribution of a one-dimensional variable. To perform clustering in multiple dimensions, algorithms of this methodology rely on one-dimensional transformations of the dataset, which limits their effectiveness. In this paper, we present M-Dip, an automatic clustering algorithm that works directly on multi-dimensional space. M-Dip also assumes that clusters correspond to different peaks in the distribution of the dataset. It separates clusters at the dips that form between neighboring peaks. Dips are detected directly in multi-dimensional space, using a graph-based method. Their statistical significance is evaluated through appropriate simulations. Our experimental evaluation indicates that M-Dip achieves significantly better results than existing algorithms based on Hartigan's dip, as well as other state-of-the-art automatic clustering algorithms.

Time series exploration and mining has many applications across several industrial and scientific domains. In this paper, we consider the problem of detecting locally similar pairs and groups, called bundles, over co-evolving time series. These are pairs or groups of subsequences whose values do not differ by more than ε for at least δ consecutive timestamps, thus indicating common local patterns and trends. We first present a baseline algorithm that performs a sweep line scan across all timestamps to identify matches. Then, we propose a filter-verification technique that only examines candidate matches at judiciously chosen checkpoints across time. Specifically, we introduce two block scanning algorithms for discovering local pairs and bundles respectively, which leverage the potential of checkpoints to aggressively prune the search space. We experimentally evaluate our methods against real-world and synthetic datasets, demonstrating a speed-up in execution time by an order of magnitude over the baseline.

A geolocated time series is a sequence of values associated with a geolocation, such as measurements provided by a sensor installed at a certain location. In this paper, we address the problem of hybrid similarity joins over such geolocated time series. This operation returns all pairs of geolocated time series that exhibit similar behavior in the time series domain while also being closely located in space. First, we propose algorithms for performing such join operations using different types of indices, including spatial-only, time series-only, and hybrid indices. Such centralized indexing schemes can cope well with moderate data volumes but they face scalability issues when the dataset size increases significantly. To overcome this problem, we present a MapReduce-based processing scheme with space-driven partitioning. Our parallel and distributed algorithm leverages our hybrid index for geolocated time series to efficiently execute similarity joins locally within each partition and minimize the amount of data that needs to be shuffled between processing nodes. An extensive experimental evaluation confirms that our approach can efficiently compute all matching pairs even for datasets containing millions of geolocated time series.

Millions of users rely on navigation applications to compute an optimal route for their trips. The basic functionality of these applications is to find the minimum cost route between a source and target node in the transportation network. In this paper, we address a variant of this problem, where the computed route is required to contain a set of Points of Interest of specific types. Our approach is based on the concept of keyword skyline. We formally define this concept, and we show how to compute the keyword skyline for the vertices of a given network and how to use it for computing the shortest keyword covering paths. We present different variants of this method, including an approximation algorithm, providing different trade-offs between preprocessing cost and execution time. Finally, we present an experimental evaluation of our approach using real-world datasets of different sizes, including also a comparison to the current state-of-the-art algorithm for this problem.

Time series associated with specific locations, such as visitor check-ins or sensor readings, have increased in size and popularity in several domains. Although several works have focused on efficient time series similarity search, there has been limited attention to the inherent challenge that geolocated time series introduce for hybrid queries on both spatial proximity and time series similarity. To efficiently process such queries, we propose a hybrid index, called TSR-tree, which extends the R-tree by introducing appropriate bounds for the time series indexed at each node. This reduces node accesses during query evaluation by simultaneously pruning the search space in the spatial domain and the time series domain while traversing the index. We also present an optimized version, the BTSR-tree, which uses tighter bounds by bundling together similar time series in each node. We describe how these indices can be used to efficiently evaluate different variants of hybrid queries combining spatial and time series filtering or ranking. Finally, we experimentally evaluate our work using real-world datasets from diverse domains, demonstrating a speed-up of 1.5 to 5 times in hybrid query workloads against the baseline R-tree method.

Betweenness centrality quantifies the importance of graph nodes in a variety of applications including social, biological and communication networks. Its computation is very costly for large graphs; therefore, many approximate methods have been proposed. Given the lack of a golden standard, the accuracy of most approximate methods is evaluated on tiny graphs and is not guaranteed to be representative of realistic datasets that are orders of magnitude larger. In this paper, we develop BeBeCA, a benchmark for betweenness centrality approximation methods on large graphs. Specifically: (i) We generate a golden standard by deploying a parallel implementation of Brandes algorithm using 96,000 CPU cores on a supercomputer to compute exact betweenness centrality values for several large graphs with up to 126M edges. (ii) We propose an evaluation methodology to assess various aspects of approximation accuracy, such as average error and quality of node ranking. (iii) We survey a large number of existing approximation methods and compare their performance and accuracy using our benchmark. (iv) We publicly share our benchmark, which includes the golden standard exact betweenness centrality values together with the scripts that implement our evaluation methodology; for researchers to compare their own algorithms and practitioners to select the appropriate algorithm for their application and data.

We study the problem of preserving user privacy in the publication of location sequences. Consider a database of trajectories, corresponding to movements of people, captured by their transactions when they use credit cards, RFID debit cards, or NFC (http://en.wikipedia.org/wiki/Near_field_communication) compliant devices. We show that, if such trajectories are published exactly (by only hiding the identities of persons that followed them), one can use partial trajectory knowledge as a quasi-identifier for the remaining locations in the sequence. We devise four intuitive techniques, based on combinations of locations suppression and trajectories splitting, and we show that they can prevent privacy breaches while keeping published data accurate for aggregate query answering and frequent subsets data mining.

In the information filtering paradigm, clients subscribe to a server with continuous queries that express their information needs and get notified every time appropriate information is published. To perform this task in an efficient way, servers employ indexing schemes that support fast matches of the incoming information with the query database. Such indexing schemes involve (i) main-memory trie-based data structures that cluster similar queries by capturing common elements between them and (ii) efficient filtering mechanisms that exploit this clustering to achieve high throughput and low filtering times. However, state-of-the-art indexing schemes are sensitive to the query insertion order and cannot adopt to an evolving query workload, degrading the filtering performance over time. In this paper, we present an adaptive trie-based algorithm that outperforms current methods by relying on query statistics to reorganise the query database. Contrary to previous approaches, we show that the nature of the constructed tries, rather than their compactness, is the determining factor for efficient filtering performance. Our algorithm does not depend on the order of insertion of queries in the database, manages to cluster queries even when clustering possibilities are limited, and achieves more than 96 percent filtering time improvement over its state-of-the-art competitors. Finally, we demonstrate that our solution is easily extensible to multi-core machines.

Water management field has concentrated great interest, with the potential to affect the long term well-being, the societal economy and security. In parallel, it imposes specific research challenges which have not been already met, due to the lack of fine-grained data. Knowledge extraction and decision making for efficient management in the energy field has attracted a lot of interest in Big Data research. However, the water domain is strikingly absent, with minimal focused work on data exploitation and useful information extraction. The goal of this work is to discover persistent and meaningful knowledge from water consumption data and provide efficient and scalable big data management and analysis services. We propose a novel methodology which exploits machine learning techniques and introduces a robust probabilistic classifier which is able to operate on data of arbitrary dimensionality and of huge volume. It also provides added value services and new operation models for the water management domain, inducing sustainable behavioural changes for consumers, which can further raise social awareness. It does so through a new k-Nearest Neighbour based algorithm, developed in a parallel and distributed environment, which operates over Big Data and discovers useful knowledge about consumption classes and other water related attitudinal properties. A detailed experimental evaluation assesses the effectiveness and efficiency of the algorithm on prediction precision along with the provision of analytics. The results show that this method is prosperous and provides accurate and interesting results that allow us to identify useful characteristics, not only for the households, but also for the water utilities.

Among the basic research tools for (bio)medical science are epidemiological studies that typically involve a number of hospitals, clinics, and research centres scattered around the world, and are often referred to as multi-centre studies. Clearly, the effectiveness and importance of a multi-centre study increases with the number of participating centres and enrolled patients, but at the same time this natural distribution in the production of research data requires sophisticated data/knowledge management infrastructures to support the participating units. This kind of infrastructure is not only expensive to build and maintain, but also cannot be reused as it is often tailored to a specific study. In this work, we present a cloud-based system, that allows users without any computer science background to design, deploy, and administer platforms aimed for managing, sharing, and analysing clinical data from multi-centre studies. The proposed system provides a zero-administration, zero-cost online data/knowledge management tool that (i) enhances re-usability by introducing study templates, (ii) supports (bio)medical needs through specialised data types able to capture specialised knowledge like repeated therapies or treatments, and (iii) emphasises data filtering/export through an expressive yet simple graphical query engine.

Publishing data about individuals, in a privacy-preserving way, has led to a large body of research. Meanwhile, algo-rithms for anonymizing datasets, with relational or trans-action attributes, that preserve data truthfulness, have at-tracted significant interest from organizations. However, se-lecting the most appropriate algorithm is still far from triv-ial, and tools that assist data publishers in this task are needed. In response, we develop SECRETA, a system for analyzing the effectiveness and efficiency of anonymization algorithms. Our system allows data publishers to evalu-ate a specific algorithm, compare multiple algorithms, and combine algorithms for anonymizing datasets with both re-lational and transaction attributes. The analysis of the algo-rithm(s) is performed, in an interactive and progressive way, and results, including attribute statistics and various data utility indicators, are summarized and presented graphically. 1.

Mining frequent subgraphs is an important operation on graphs; it is defined as finding all subgraphs that appear frequently in a database according to a given frequency threshold. Most existing work assumes a database of many small graphs, but modern applications, such as social networks, citation graphs, or protein-protein interactions in bioinformatics, are modeled as a single large graph. In this paper we present GraMi, a novel framework for frequent subgraph mining in a single large graph. GraMi undertakes a novel approach that only finds the minimal set of instances to satisfy the frequency threshold and avoids the costly enumeration of all instances required by previous approaches. We accompany our approach with a heuristic and optimizations that significantly improve performance. Additionally, we present an extension of GraMi that mines frequent patterns. Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Finally, we present CGraMi, a version supporting structural and semantic constraints, and AGraMi, an approximate version producing results with no false positives. Our experiments on real data demonstrate that our framework is up to 2 orders of magnitude faster and discovers more interesting patterns than existing approaches.

The publication of trajectory data opens up new directions in studying human behavior, but it is challenging to perform in a privacy-preserving way. This is mainly because, the identities of individuals, whose movement is recorded in the data, can be disclosed, even after removing identifying information. Existing works to anonymize trajectory data offer privacy, but at a high data utility cost. This is because, they either do not produce truthful data, which is important in many applications, or are limited in their privacy specification component. This paper proposes an approach that overcomes these shortcomings by adapting km-anonymity to trajectory data and by using distance-based generalization. We also develop an effective and efficient anonymization algorithm, which is based on the apriori principle. Our experiments verify that this algorithm preserves data utility well, and it is fast and scalable.

In this work, we focus on protection against identity disclosure in the publication of sparse multidimensional data. Existing multidimensional anonymization techniques (a) protect the privacy of users either by altering the set of quasi-identifiers of the original data (e.g., by generalization or suppression) or by adding noise (e.g., using differential privacy) and/or (b) assume a clear distinction between sensitive and non-sensitive information and sever the possible linkage. In many real world applications the above techniques are not applicable. For instance, consider web search query logs. Suppressing or generalizing anonymization methods would remove the most valuable information in the dataset: the original query terms. Additionally, web search query logs contain millions of query terms which cannot be categorized as sensitive or non-sensitive since a term may be sensitive for a user and non-sensitive for another. Motivated by this observation, we propose an anonymization technique termed disassociation that preserves the original terms but hides the fact that two or more different terms appear in the same record. We protect the users' privacy by disassociating record terms that participate in identifying combinations. This way the adversary cannot associate with high probability a record with a rare combination of terms. To the best of our knowledge, our proposal is the first to employ such a technique to provide protection against identity disclosure . We propose an anonymization algorithm based on our approach and evaluate its performance on real and synthetic datasets, comparing it against other state-of-the-art methods based on generalization and differential privacy.

The recent advances in the infrastructure of Geographic Information Systems (GIS), and the proliferation of GPS technology, have resulted in the abundance of geodata in the form of sequences of points of interest (POIs), waypoints, etc. We refer to sets of such sequences as route collections. In this work, we consider path queries on frequently updated route collections: given a route collection and two points nsand nt, a path query returns a path, i.e., a sequence of points, that connects nsto nt. We introduce two path query evaluation paradigms that enjoy the benefits of search algorithms (i.e., fast index maintenance) while utilizing transitivity information to terminate the search sooner. Efficient indexing schemes and appropriate updating procedures are introduced. An extensive experimental evaluation verifies the advantages of our methods compared to conventional graph-based search.

The suffix tree is a data structure for indexing strings. It is used in a variety of applications such as bioinformatics, time series analysis, clustering, text editing and data compression. However, when the string and the resulting suffix tree are too large to fit into the main memory, most existing construction algorithms become very inefficient. This paper presents a disk-based suffix tree construction method, called Elastic Range (ERa), which works efficiently with very long strings that are much larger than the available memory. ERa partitions the tree construction process horizontally and vertically and minimizes I/Os by dynamically adjusting the horizontal partitions independently for each vertical partition, based on the evolving shape of the tree and the available memory. Where appropriate, ERa also groups vertical partitions together to amortize the I/O cost. We developed a serial version; a parallel version for shared-memory and shared-disk multi-core systems; and a parallel version for shared-nothing architectures. ERa indexes the entire human genome in 19 minutes on an ordinary desktop computer. For comparison, the fastest existing method needs 15 minutes using 1024 CPUs on an IBM BlueGene supercomputer.

This paper introduces a reasoning system based on a previously developed model for ternary projective relations between spatial objects. The model applies to spatial objects of the kind point and region is based on basic projective invariants and takes into account the size and shape of the three objects that are involved in a relation. The reasoning system proposes a set of permutation and composition rules, which allow the inference of unknown relations from given ones.

The peer-to-peer (P2P) paradigm has become very popular for storing and sharing information in a totally decentralized manner. At first, research focused on P2P systems that host 1D data. Nowadays, the need for P2P applications with multidimensional data has emerged, motivating research on P2P systems that manage such data. The majority of the proposed techniques are based either on the distribution of centralized indexes or on the reduction of multidimensional data to one dimension. Our goal is to create from scratch a technique that is inherently distributed and also maintains the multidimensionality of data. Our focus is on structured P2P systems that share spatial information. We present SpatialP2P, a totally decentralized indexing and searching framework that is suitable for spatial data. SpatialP2P supports P2P applications in which spatial information of various sizes can be dynamically inserted or deleted, and peers can join or leave. The proposed technique preserves well locality and directionality of space.

Active data warehousing has emerged as an alternative to conventional warehousing practices in order to meet the high demand of applications for up-to-date information. In a nutshell, an active warehouse is refreshed online and thus achieves a higher consistency between the stored information and the latest data updates. The need for online warehouse refreshment introduces several challenges in the implementation of data warehouse transformations, with respect to their execution time and their overhead to the warehouse processes. In this paper, we focus on a frequently encountered operation in this context, namely, the join of a fast stream 5" of source updates with a disk-based relation R, under the constraint of limited memory. This operation lies at the core of several common transformations such as surrogate key assignment, duplicate detection, or identification of newly inserted tuples. We propose a specialized join algorithm, termed mesh join (MESHJOIN), which compensates for the difference in the access cost of the two join inputs by 1) relying entirely on fast sequential scans of R and 2) sharing the I/O cost of accessing R across multiple tuples of 5". We detail the MESHJOIN algorithm and develop a systematic cost model that enables the tuning of MESHJOIN for two objectives: maximizing throughput under a specific memory budget or minimizing memory consumption for a specific throughput. We present an experimental study that validates the performance of MESHJOIN on synthetic and real-life data. Our results verify the scalability of MESHJOIN to fast streams and large relations and demonstrate its numerous advantages over existing join algorithms.

Active data warehousing has emerged as an alternative to conventional warehousing practices in order to meet the high demand of applications for up-to-date information. In a nutshell, an active warehouse is refreshed on-line and thus achieves a higher consistency between the stored information and the latest data updates. The need for on-line warehouse refreshment introduces several challenges in the implementation of data warehouse transformations, with respect to their execution time and their overhead to the warehouse processes. In this paper, we focus on a frequently encountered operation in this context, namely, the join of a fast stream S of source updates with a disk-based relation R, under the constraint of limited memory. This operation lies at the core of several common transformations, such as, surrogate key assignment, duplicate detection or identification of newly inserted tuples. We propose a specialized join algorithm, termed mesh join (MeshJoin), that compensates for the difference in the access cost of the two join inputs by (a) relying entirely on fast sequential scans of R, and (b) sharing the I/O cost of accessing R across multiple tuples of S. We detail the Mesh Join algorithm and develop a systematic cost model that enables the tuning of Mesh Join for two objectives: maximizing throughput under a specific memory budget or minimizing memory consumption for a specific throughput. We present an experimental study that validates the performance of Mesh Join on synthetic and real-life data. Our results verify the scalability of Mesh-Join to fast streams and large relations, and demonstrate its numerous advantages over existing join algorithms.

Nowadays, mobile users with global positioning devices canaccess Location Based Services (LBS) and query about pointsof interest in their proximity. For such applications to succeed,privacy and confidentiality are essential. Encryptionalone is not adequate; although it safeguards the systemagainst eavesdroppers, the queries themselves may disclosethe location and identity of the user. Recently, there havebeen proposed centralized architectures based on K-anonymity,which utilize an intermediate anonymizer between themobile users and the LBS. However, the anonymizer mustbe updated continuously with the current locations of allusers. Moreover, the complete knowledge of the entire systemposes a security threat, if the anonymizer is compromised.In this paper we address two issues: (i) We show thatexisting approaches may fail to provide spatial anonymityfor some distributions of user locations and describe a noveltechnique which solves this problem. (ii) We propose Prive,a decentralized architecture for preserving the anonymityof users issuing spatial queries to LBS. Mobile users self-organizeinto an overlay network with good fault toleranceand load balancing properties. Prive avoids the bottleneckcaused by centralized techniques both in terms of anonymizationand location updates. Moreover, the system state isdistributed in numerous users, rendering Prive resilient toattacks. Extensive experimental studies suggest that Priveis applicable to real-life scenarios with large populations ofmobile users.

In this paper, we introduce a family of expressive models for qualitative spatial reasoning with directions. The proposed family is based on the cognitive plausible cone-based model. We formally define the directional relations that can be expressed in each model of the family. Then, we use our formal framework to study two interesting problems: computing the inverse of a directional relation and composing two directional relations. For the composition operator, in particular, we concentrate on two commonly used definitions, namely, consistency-based and existential composition. Our formal framework allows us to prove that our solutions are correct. The presented solutions are handled in a uniform manner and apply to all of the models of the family.

In the above titled paper (ibid., vol. 19, no. 8, pp. 1116-1130, Aug 07), some information appeared incorrectly. The corrections appear here.

We study the complexity of query satisfiability and entailment for the Boolean information retrieval models WP and AWV using techniques from propositional logic and computational complexity. WP and AWV can be used to represent and query textual information under the Boolean model using the concept of attribute with values of type text, the concept of word, and word proximity constraints. Variations of WP and AWP are in use in most deployed digital libraries using the Boolean model, text extenders for relational database systems (e.g., Oracle 10g), search engines, and P2P systems for information retrieval and filtering

Qualitative spatial reasoning forms an important part of the commonsense reasoning required for building intelligent geographical information systems (GIS). Previous research has come up with models to capture cardinal direction relations for typical GIS data. In this paper, we target the problem of efficiently computing the cardinal direction relations between regions that are composed of sets of polygons and present two algorithms for this task. The first of the proposed algorithms is purely qualitative and computes, in linear time, the cardinal direction relations between the input regions. The second has a quantitative aspect and computes, also in linear time, the cardinal direction relations with percentages between the input regions. Our experimental evaluation indicates that the proposed algorithms outperform existing methodologies. The algorithms have been implemented and embedded in an actual system, CARDIRECT, that allows the user to 1) specify and annotate regions of interest in an image or a map, 2) compute cardinal direction relations between them, and 3) pose queries in order to retrieve combinations of interesting regions.

Data visualization is one of the big issues of database research. OLAP as a decision support technology is highly related to the developments of data visualization area. In this paper we demonstrate how the Cube Presentation Model (CPM), a novel presentational model for OLAP screens, can be naturally mapped on the Table Lens, which is an advanced visualization technique from the Human-Computer Interaction area, particularly tailored for cross-tab reports. We consider how the user interacts with an OLAP screen and based on the particularities of Table Lens, we propose an automated proactive users support. Finally, we discuss the necessity and the applicability of advanced visualization techniques in the presence of recent technological developments. Copyright 2003 ACM.

Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. In this paper, we focus on the problem of the definition of ETL activities and provide formal foundations for their conceptual representation. The proposed conceptual model is (a) customized for the tracing of inter-attribute relationships and the respective ETL activities in the early stages of a data warehouse project; (b) enriched with a 'palette' of a set of frequently used ETL activities, like the assignment of surrogate keys, the check for null values, etc; and (c) constructed in a customizable and extensible manner, so that the designer can enrich it with his own re-occurring patterns for ETL activities.