Subgraph counting, which involves determining the frequency of a query graph within a data graph, has numerous applications such as query optimization, fraud detection, and evaluating the expressiveness of graph neural networks. Despite its importance, there has been no systematic study on the impact of adversarial graph perturbations on subgraph counts. In this work, we examine the kSub problem, which aims to identify k edge additions that maximize the count of a query graph. We prove that kSub is intractable due to its NP-hardness, even for constant approximation. To address this, we relax the problem into a top-k selection, termed topkSub. Depending on the structure of the relaxed query graph, we distinguish two possible processing scenarios (namely, the connected and the disconnected case), and design dedicated search space pruning strategies for each scenario. Additionally, we develop sampling techniques on the pruned search space to scale topkSub for handling large graphs. In the form of a case study, we demonstrate that topkSub effectively uncovers vulnerabilities in state-of-the-art GNN models for subgraph counting, providing a significant advantage over alternative graph perturbation methods. The efficiency analysis shows that our pruning techniques achieve substantial speedups for exact processing in both connected and disconnected cases, while our sampling methods reduce estimation errors by 1-2 orders of magnitude compared to state-of-the-art samplers.

The two paradigms to identify records of preference in a multi-objective setting rely either on dominance (e.g., the skyline operator) or on a utility function defined over the records’ attributes (typically using a top- k query). Despite their proliferation, each has its own palpable drawbacks. Motivated by these drawbacks, we identify three hard requirements for practical decision support, namely, personalization, controllable output size, and flexibility in preference specification. With these requirements as a guide, we combine elements from both paradigms and propose two new operators, ORD and ORU . We present a suite of algorithms for their efficient processing, dedicating more technical effort to ORU , whose nature is inherently more challenging. Specifically, besides a sophisticated algorithm for ORD , we describe two exact methods for ORU and one approximate. We perform a qualitative study to demonstrate how our operators work and evaluate the performance of our algorithms against adaptations of previous work that mimic their output.

An outstanding fact (OF) is a striking claim by which some entities stand out from their peers on some attribute. OFs serve data journalism, fact checking, and recommendation. However, one could jump to conclusions by selecting truthful OFs while intentionally or inadvertently ignoring lateral contexts and data that render them less striking. This jumping conclusion bias from unstable OFs may disorient the public, including voters and consumers, raising concerns about fairness and transparency in political and business competition. It is thus ethically imperative for several stakeholders to measure the robustness of OFs with respect to lateral contexts and data. Unfortunately, a capacity for such inspection of OFs mined from knowledge graphs (KGs) is missing. In this paper, we propose a methodology that inspects the robustness of OFs in KGs by perturbation analysis. We define (1) entity perturbation, which detects outlying contexts by perturbing context entities in the OF; and (2) data perturbation, which considers plausible data that render an OF less striking. We compute the expected strikingness scores of OFs over perturbation relevance distributions and assess an OF as robust if its measured strikingness does not deviate significantly from the expected. We devise a suite of exact and sampling algorithms for perturbation analysis on large KGs. Extensive experiments reveal that our methodology accurately and efficiently detects frail OFs generated by existing mining approaches on KGs. We also show the effectiveness of our approaches through case and user studies.

In this paper, we study the m-impact region problem (mIR). In a context where users look for available products with top-k queries, mIR identifies the part of the product space that attracts the most user attention. Specifically, mIR determines the kind of attribute values that lead a (new or existing) product to the top-k result for at least a fraction of the user population. mIR has several applications, ranging from effective marketing to product improvement. Importantly, it also leads to (exact and efficient) solutions for standing top-k impact problems, which were previously solved heuristically only, or whose current solutions face serious scalability limitations. We experiment, among others, on data mined from actual user reviews for real products, and demonstrate the practicality and efficiency of our algorithms, both for mIR and for standing top-k impact problems.

The two most common paradigms to identify records of preference in a multi-objective setting rely either on dominance (e.g., the skyline operator) or on a utility function defined over the records' attributes (typically, using a top-k query). Despite their proliferation, each of them has its own palpable drawbacks. Motivated by these drawbacks, we identify three hard requirements for practical decision support, namely, personalization, controllable output size, and flexibility in preference specification. With these requirements as a guide, we combine elements from both paradigms and propose two new operators, ORD and ORU. We perform a qualitative study to demonstrate how they work, and evaluate their performance against adaptations of previous work that mimic their output.

The top-k query has been studied extensively, and is considered the norm for multi-criteria decision making in large databases. In recent years, research has considered several complementary operators to the traditional top-k query, drawing inspiration (both in terms of problem formulation and solution design) from the geometric nature of the top-k processing model. In this seminar, we will present advances in that stream of work, focusing on updates since the preliminary seminar on the same topic in MDM'16.

Top- k queries are extensively used to retrieve the k most relevant options (e.g., products, services, accommodation alternatives, etc) based on a weighted scoring function that captures user preferences. In this paper, we take the viewpoint of a business owner who plans to introduce a new option to the market, with a certain type of clientele in mind. Given a target region in the consumer spectrum, we determine what attribute values the new option should have, so that it ranks among the top- k for any user in that region. Our methodology can also be used to improve an existing option, at the minimum modification cost, so that it ranks consistently high for an intended type of customers. This is the first work on competitive option placement where no distinct user(s) are targeted, but a general clientele type, i.e., a continuum of possible preferences. Here also lies our main challenge (and contribution), i.e., dealing with the interplay between two continuous spaces: the targeted region in the preference spectrum, and the option domain (where the new option will be placed). At the core of our methodology lies a novel and powerful interlinking between the two spaces. Our algorithms offer exact answers in practical response times, even for the largest of the standard benchmark datasets.

Traditional rank-aware processing assumes a dataset that contains available options to cover a specific need (e.g., restaurants, hotels, etc) and users who browse that dataset via top- k queries with linear scoring functions, i.e., by ranking the options according to the weighted sum of their attributes, for a set of given weights. In practice, however, user preferences (weights) may only be estimated with bounded accuracy, or may be inherently uncertain due to the inability of a human user to specify exact weight values with absolute accuracy. Motivated by this, we introduce the uncertain top-k query ( UTK ). Given uncertain preferences, that is, an approximate description of the weight values, the UTK query reports all options that may belong to the top- k set. A second version of the problem additionally reports the exact top- k set for each of the possible weight settings. We develop a scalable processing framework for both UTK versions, and demonstrate its efficiency using standard benchmark datasets.

202402 bcch

In rank-aware processing, user preferences are typically represented by a numeric weight per data attribute, collectively forming a weight vector. The score of an option (data record) is defined as the weighted sum of its individual attributes. The highest-scoring options across a set of alternatives (dataset) are shortlisted for the user as the recommended ones. In that setting, the user input is a vector (equivalently, a point) in a d-dimensional preference space, where d is the number of data attributes. In this paper we study the problem of determining in which regions of the preference space the weight vector should lie so that a given option (focal record) is among the top-k score-wise. In effect, these regions capture all possible user profiles for which the focal record is highly preferable, and are therefore essential in market impact analysis, potential customer identification, profile-based marketing, targeted advertising, etc. We refer to our problem as k-Shortlist Preference Region identification (kSPR), and exploit its computational geometric nature to develop a framework for its efficient (and exact) processing. Using real and synthetic benchmarks, we show that our most optimized algorithm outperforms by three orders of magnitude a competitor we constructed from previous work on a different problem.

Top- k processing is a well-studied problem with numerous applications that is becoming increasingly relevant with the growing availability of recommendation systems and decision making software. The objective of this tutorial is twofold. First, we will delve into the geometric aspects of top- k processing. Second, we will cover complementary features to top- k queries, with strong practical relevance and important applications, that have a computational geometric nature. The tutorial will close with insights in the effect of dimensionality on the meaningfulness of top- k queries, and interesting similarities to nearest neighbor search.

The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. Our objective is to support large numbers of users and high stream rates, while refreshing the top-k results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach. Instead, it follows an identifier-ordering paradigm that suits better the nature of the problem. When complemented with a novel, locally adaptive technique, our method offers (i) proven optimality w.r.t. the number of considered queries per stream event, and (ii) an order of magnitude shorter response time (i.e., time to refresh the query results) than the current state-of-the-art.

The diffusion of social networks introduces new challenges and opportunities for advanced services, especially so with their ongoing addition of location-based features. We show how applications like company and friend recommendation could significantly benefit from incorporating social and spatial proximity, and study a query type that captures these two-fold semantics. We develop highly scalable algorithms for its processing, and use real social network data to empirically verify their efficiency and efficacy.

The top-kquery is a common means to shortlist a number of options from a set of alternatives, based on the user's preferences. Typically, these preferences are expressed as a vector of query weights, defined over the options' attributes. The query vector implicitly associates each alternative with a numeric score, and thus imposes a ranking among them. The top-kresult includes thekoptions with the highest scores. In this context, we define themaximum rankquery (MaxRank). Given a focal option in a set of alternatives, theMaxRankproblem is to compute the highest rank this option may achieve under any possible user preference, and furthermore, to report all the regions in the query vector's domain where that rank is achieved.MaxRankfinds application in market impact analysis, customer profiling, targeted advertising, etc. We propose a methodology forMaxRankprocessing and evaluate it with experiments on real and benchmark synthetic datasets.

The diffusion of social networks introduces new challenges and opportunities for advanced services, especially so with their ongoing addition of location-based features. We show how applications like company and friend recommendation could significantly benefit from incorporating social and spatial proximity, and study a query type that captures these two-fold semantics. We develop highly scalable algorithms for its processing, and enhance them with elaborate optimizations. Finally, we use real social network data to empirically verify the efficiency and efficacy of our solutions.

Historical traffic information is valuable for transportation analysis and planning, as well as for route search services. In view of these applications, we propose the k traffic-tolerant paths problem (TTP) on road networks, which takes a source-destination pair and historical traffic information as input, and returns k paths that minimize the aggregate (historical) travel time. Unlike the shortest path problem, the TTP problem has a combinatorial search space that renders the optimal solution expensive to compute. We propose an exact algorithm and a heuristic algorithm for this problem. Experiments on real traffic data demonstrate the effectiveness of TTP paths and the efficiency of our proposed algorithms.

A top-k query shortlists the k records in a dataset that best match the user's preferences. To indicate her preferences, the user typically determines a numeric weight for each data dimension (i.e., attribute). We refer to these weights collectively as the query vector. Based on this vector, each data record is implicitly mapped to a score value (via a weighted sum function). The records with the k largest scores are reported as the result. In this paper we propose an auxiliary feature to standard top-k query processing. Specifically, we compute the maximal locus within which the query vector incurs no change in the current top-k result. In other words, we compute all possible query weight settings that produce exactly the same top-k result as the user's original query. We call this locus the global immutable region (GIR). The GIR can be used as a guide to query vector readjustments, as a sensitivity measure for the top-k result, as well as to enable effective result caching. We develop efficient algorithms for GIR computation, and verify their robustness using a variety of real and synthetic datasets.

In this paper we identify a novel query type, the top-K assignment query (αTop-K). Consider a set of objects and a set of suppliers, where each object must be assigned to one supplier. Assume that there is a cost associated with every object-supplier pair. If we allocate each object to the server with the smallest cost (for the specific object), the derived overall assignment will have the minimum total cost. In many scenarios, however, runner-up assignments may be required too, like for example when a decision maker needs to make additional considerations, not captured by individual object-supplier costs. In this case, it is necessary to examine several shortlisted assignments before choosing one. This motivates the αTop-K query, which computes the K best assignments, i.e., those achieving the K smallest total costs. Algorithms for the traditional assignment ranking problem could be adapted to process the query, but their time requirements are prohibitive for large datasets (cubic to the input size). In this work we exploit the specific properties of the αTop-K problem and develop scalable methods for its processing. We also consider its incremental version, where K is not specified in advance; instead, the best assignments are iteratively computed on demand. An empirical evaluation with real data verifies the practicality and efficiency of our framework.

Users of databases that are hosted on shared servers cannot take for granted that their queries will not be disclosed to unauthorized parties. Even if the database is encrypted, an adversary who is monitoring the I/O activity on the server may still be able to infer some information about a user query. For the particular case of a B+-tree that has its nodes encrypted, we identify properties that enable the ordering among the leaf nodes to be deduced. These properties allow us to construct adversarial algorithms to recover the B+-tree structure from the I/O traces generated by range queries. Combining this structure with knowledge of the key distribution (or the plaintext database itself), the adversary can infer the selection range of user queries. To counter the threat, we propose a privacy-enhancing PB+-tree index which ensures that there is high uncertainty about what data the user has worked on, even to a knowledgeable adversary who has observed numerous query executions. The core idea in PB+-tree is to conceal the order of the leaf nodes in an encrypted B+-tree. In particular, it groups the nodes of the tree into buckets, and employs homomorphic encryption techniques to prevent the adversary from pinpointing the exact nodes retrieved by range queries. PB+-tree can be tuned to balance its privacy strength with the computational and I/O overheads incurred. Moreover, it can be adapted to protect access privacy in cases where the attacker additionally knows a priori the access frequencies of key values. Experiments demonstrate that PB+-tree effectively impairs the adversary's ability to recover the B+-tree structure and deduce the query ranges in all considered scenarios.

Given a high-dimensional dataset, a top-kquery can be used to shortlist thektuples that best match the user's preferences. Typically, these preferences regard a subset of the available dimensions (i.e., attributes) whose relative significance is expressed by user-specified weights. Along with the query result, we propose to compute for each involved dimension the maximal deviation to the corresponding weight for which the query result remains valid. The derived weight ranges, called immutable regions, are useful for performing sensitivity analysis, for finetuning the query weights, etc. In this paper, we focus on top-kqueries with linear preference functions over the queried dimensions. We codify the conditions under which changes in a dimension's weight invalidate the query result, and develop algorithms to compute the immutable regions. In general, this entails the examination of numerous non-result tuples. To reduce processing time, we introduce a pruning technique and a thresholding mechanism that allow the immutable regions to be determined correctly after examining only a small number of non-result tuples. We demonstrate empirically that the two techniques combine well to form a robust and highly resource-efficient algorithm. We verify the generality of our findings using real high-dimensional data from different domains (documents, images, etc) and with different characteristics.

Shortest path computation is one of the most common queries in location-based services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client's position and her destination may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. The only existing method for privacy-preserving shortest path computation follows the obfuscation paradigm; it prevents the LBS from inferring the source and destination of the query with a probability higher than a threshold. This implies, however, that the LBS still deduces some information (albeit not exact) about the client's location and her destination. In this paper we aim at strong privacy, where the adversary learns nothing about the shortest path query. We achieve this via established private information retrieval techniques, which we treat as black-box building blocks. Experiments on real, large-scale road networks assess the practicality of our schemes.

Consider a text filtering server that monitors a stream of incoming documents for a set of users, who register their interests in the form of continuous text search queries. The task of the server is to constantly maintain for each query a ranked result list, comprising the recent documents (drawn from a sliding window) with the highest similarity to the query. Such a system underlies many text monitoring applications that need to cope with heavy document traffic, such as news and email monitoring. In this paper, we propose the first solution for processing continuous text queries efficiently. Our objective is to support a large number of user queries while sustaining high document arrival rates. Our solution indexes the streamed documents in main memory with a structure based on the principles of the inverted file, and processes document arrival and expiration events with an incremental threshold-based method. We distinguish between two versions of the monitoring algorithm, an eager and a lazy one, which differ in how aggressively they manage the thresholds on the inverted index. Using benchmark queries over a stream of real documents, we experimentally verify the efficiency of our methodology; both its versions are at least an order of magnitude faster than a competitor constructed from existing techniques, with lazy being the best approach overall.

Research on spatial network databases has so far considered that there is a single cost value associated with each road segment of the network. In most real-world situations, however, there may exist multiple cost types involved in transportation decision making. For example, the different costs of a road segment could be its Euclidean length, the driving time, the walking time, possible toll fee, etc. The relative significance of these cost types may vary from user to user. In this paper we consider such multi-cost transportation networks (MCN), where each edge (road segment) is associated with multiple cost values. We formulate skyline and top-k queries in MCNs and design algorithms for their efficient processing. Our solutions have two important properties in preference-based querying; the skyline methods are progressive and the top-k ones are incremental. The performance of our techniques is evaluated with experiments on a real road network.

Shortest path search in transportation networks is unarguably one of the most important online search services nowadays (e.g., Google Maps, MapQuest, etc), with applications spanning logistics, spatial optimization, or everyday driving decisions. Often times, the owner of the road network data (e.g., a transport authority) provides its database to third-party query services, which are responsible for answering shortest path queries posed by their clients. The issue arising here is that a query service might be returning sub-optimal paths either purposely (in order to serve its own purposes like computational savings or commercial reasons) or because it has been compromised by Internet attackers who falsify the results. Therefore, for the above applications to succeed, it is essential that each reported path is accompanied by a proof, which allows clients to verify the path's correctness. This is the first study on shortest path verification in outsourced network databases. We propose the concept of authenticated hints, which is used to reduce the size of the proofs. We develop several authentication techniques and quantify their tradeoffs with respect to offline construction cost and proof size. Experiments on real road networks demonstrate that our solutions are indeed efficient and lead to compact query proofs.

Shortest path computation is one of the most common queries in location-based services that involve transportation networks. Motivated by scalability challenges faced in the mobile network industry, we propose adopting the wireless broadcast model for such location-dependent applications. In this model the data are continuously transmitted on the air, while clients listen to the broadcast and process their queries locally. Although spatial problems have been considered in this environment, there exists no study on shortest path queries in road networks. We develop the first framework to compute shortest paths on the air, and demonstrate the practicality and efficiency of our techniques through experiments with real road networks and actual device specifications.

The increasing availability of location-aware mobile devices has given rise to a flurry of location-based services (LBSs). Due to the nature of spatial queries, an LBS needs the user position in order to process her requests. On the other hand, revealing exact user locations to a (potentially untrusted) LBS may pinpoint their identities and breach their privacy. To address this issue, spatial anonymity techniques obfuscate user locations, forwarding to the LBS a sufficiently large region instead. Existing methods explicitly target processing in the euclidean space and do not apply when proximity to the users is defined according to network distance (e.g., driving time through the roads of a city). In this paper, we propose a framework for anonymous query processing in road networks. We design location obfuscation techniques that: (1) provide anonymous LBS access to the users and (2) allow efficient query processing at the LBS side. Our techniques exploit existing network database infrastructure, requiring no specialized storage schemes or functionalities. We experimentally compare alternative designs in real road networks and demonstrate the effectiveness of our techniques.

The concept of k-anonymity has received considerable attention due to the need of several organizations to release microdata without revealing the identity of individuals. Although all previous k-anonymity techniques assume the existence of a public database (PD) that can be used to breach privacy, none utilizes PD during the anonymization process. Specifically, existing generalization algorithms create anonymous tables using only the microdata table (MT) to be published, independently of the external knowledge available. This omission leads to high information loss. Motivated by this observation, we first introduce the concept of k-join-anonymity (KJA), which permits more effective generalization to reduce the information loss. Briefly, KJA anonymizes a superset of MT, which includes selected records from PD. We propose two methodologies for adapting k-anonymity algorithms to their KJA counterparts. The first generalizes the combination of MT and PD, under the constraint that each group should contain at least 1 tuple of MT (otherwise, the group is useless and discarded). The second anonymizes MT, and then, refines the resulting groups using PD. Finally, we evaluate the effectiveness of our contributions with an extensive experimental evaluation using real and synthetic data sets.

Consider a set of customers (e.g., WiFi receivers) and a set of service providers (e.g., wireless access points), where each provider has a capacity and the quality of service offered to its customers is anti-proportional to their distance. The Capacity Constrained Assignment (CCA) is a matching between the two sets such that (i) each customer is assigned to at most one provider, (ii) every provider serves no more customers than its capacity, (iii) the maximum possible number of customers are served, and (iv) the sum of Euclidean distances within the assigned provider-customer pairs is minimized. Although max-flow algorithms are applicable to this problem, they require the complete distance-based bipartite graph between the customer and provider sets. For large spatial datasets, this graph is expensive to compute and it may be too large to fit in main memory. Motivated by this fact, we propose efficient algorithms for optimal assignment that employ novel edge-pruning strategies, based on the spatial properties of the problem. Additionally, we develop incremental techniques that maintain an optimal assignment (in the presence of updates) with a processing cost several times lower than CCA recomputation from scratch. Finally, we present approximate (i.e., suboptimal) CCA solutions that provide a tunable trade-off between result accuracy and computation cost, abiding by theoretical quality guarantees. A thorough experimental evaluation demonstrates the efficiency and practicality of the proposed techniques.

A text filtering system monitors a stream of incoming documents, to identify those that match the interest profiles of its users. The user interests are registered at a server as continuous text search queries. The server constantly maintains for each query a ranked result list, comprising the recent documents (drawn from a sliding window) with the highest similarity to the query. Such a system underlies many text monitoring applications that need to cope with heavy document traffic, such as news and email monitoring. In this paper, we propose the first solution for processing continuous text queries efficiently. Our objective is to support a large number of user queries while sustaining high document arrival rates. Our solution indexes the streamed documents with a structure based on the principles of the inverted file, and processes document arrival and expiration events with an incremental threshold-based method. Using a stream of real documents, we experimentally verify the efficiency of our approach, which is at least an order of magnitude faster than a competitor constructed from existing techniques.

Consider multiple users searching for a hotel room, based on size, cost, distance to the beach, etc. Users may have variable preferences expressed by different weights on the attributes of the searched objects. Although individual preference queries can be evaluated by selecting the object in the database with the highest aggregate score, in the case of multiple requests at the same time, a single object cannot be assigned to more than one users. The challenge is to compute a fair 1-1 matching between the queries and a subset of the objects. We model this as a stable-marriage problem and propose an efficient technique for its evaluation. Our algorithm is an iterative process, which finds at each step the query-object pair with the highest score and removes it from the problem. This is done efficiently by maintaining and matching the skyline of the remaining objects with the remaining queries at each step. An experimental evaluation with synthetic and real data confirms the effectiveness of our method.

Query answers from servers operated by third parties need to be verified, as the third parties may not be trusted or their servers may be compromised. Most of the existing authentication methods construct validity proofs based on the Merkle hash tree (MHT). The MHT, however, imposes severe concurrency constraints that slow down data updates. We introduce a protocol, built upon signature aggregation, for checking the authenticity, completeness and freshness of query answers. The protocol offers the important property of allowing new data to be disseminated immediately , while ensuring that outdated values beyond a pre-set age can be detected. We also propose an efficient verification technique for ad-hoc equijoins, for which no practical solution existed. In addition, for servers that need to process heavy query workloads, we introduce a mechanism that significantly reduces the proof construction time by caching just a small number of strategically chosen aggregate signatures. The efficiency and efficacy of our proposed mechanisms are confirmed through extensive experiments.

Consider an internship assignment system, where at the end of each academic year, interested university students search and apply for available positions, based on their preferences (e.g., nature of the job, salary, office location, etc). In a variety of facility, task or position assignment contexts, users have personal preferences expressed by different weights on the attributes of the searched objects. Although individual preference queries can be evaluated by selecting the object in the database with the highest aggregate score, in the case of multiple simultaneous requests, a single object cannot be assigned to more than one users. The challenge is to compute a fair 1--1 matching between the queries and the objects. We model this as a stable-marriage problem and propose an efficient method for its processing. Our algorithm iteratively finds stable query-object pairs and removes them from the problem. At its core lies a novel skyline maintenance technique, which we prove to be I/O optimal. We conduct an extensive experimental evaluation using real and synthetic data, which demonstrates that our approach outperforms adaptations of previous methods by several orders of magnitude.

We consider an environment of numerous moving objects, equipped with location-sensing devices and capable of communicating with a central coordinator. In this setting, we investigate the problem of maintaining hot motion paths, i.e., routes frequently followed by multiple objects over the recent past. Motion paths approximate portions of objects' movement within a tolerance margin that depends on the uncertainty inherent in positional measurements. Discovery of hot motion paths is important to applications requiring classification/profiling based on monitored movement patterns, such as targeted advertising, resource allocation, etc. To achieve this goal, we delegate part of the path extraction process to objects, by assigning to them adaptive lightweight filters that dynamically suppress unnecessary location updates and, thus, help reducing the communication overhead. We demonstrate the benefits of our methods and their efficiency through extensive experiments on synthetic data sets.

Given a point set P of customers (e.g., WiFi receivers) and a point set Q of service providers (e.g., wireless access points), where each q ∈ Q has a capacity q.k, the capacity constrained assignment (CCA) is a matching M ⊆ Q × P such that (i) each point q ∈ Q (p ∈ P) appears at most k times (at most once) in M, (ii) the size of M is maximized (i.e., it comprises min{|P|, ∑q∈Qq.k} pairs), and (iii) the total assignment cost (i.e., the sum of Euclidean distances within all pairs) is minimized. Thus, the CCA problem is to identify the assignment with the optimal overall quality; intuitively, the quality of q's service to p in a given (q, p) pair is anti-proportional to their distance. Although max-flow algorithms are applicable to this problem, they require the complete distance-based bipartite graph between Q and P. For large spatial datasets, this graph is expensive to compute and it may be too large to fit in main memory. Motivated by this fact, we propose efficient algorithms for optimal assignment that employ novel edge-pruning strategies, based on the spatial properties of the problem. Additionally, we develop approximate (i.e., suboptimal) CCA solutions that provide a trade-off between result accuracy and computation cost, abiding by theoretical quality guarantees. A thorough experimental evaluation demonstrates the efficiency and practicality of the proposed techniques.

The number of successful attacks on the Internet shows that it is very difficult to guarantee the security of online search engines. A breached server that is not detected in time may return incorrect results to the users. To prevent that, we introduce a methodology for generating an integrity proof for each search result. Our solution is targeted at search engines that perform similarity-based document retrieval, and utilize an inverted list implementation (as most search engines do). We formulate the properties that define a correct result, map the task of processing a text search query to adaptations of existing threshold-based algorithms, and devise an authentication scheme for checking the validity of a result. Finally, we confirm the efficiency and practicality of our solution through an empirical evaluation with real documents and benchmark queries.

The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of location-based services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to location-based services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. Our methods optimize the entire process of anonymizing the requests and processing the transformed spatial queries. Extensive experimental studies suggest that the proposed techniques are applicable to real-life scenarios with numerous mobile users.

This paper studies continuous monitoring of nearest neighbor (NN) queries over sliding window streams. According to this model, data points continuously stream in the system, and they are considered valid only while they belong to a sliding window that contains 1) the W most recent arrivals (count-based) or 2) the arrivals within a fixed interval W covering the most recent time stamps (time-based). The task of the query processor is to constantly maintain the result of long-running NN queries among the valid data. We present two processing techniques that apply to both count-based and time-based windows. The first one adapts conceptual partitioning, the best existing method for continuous NN monitoring over update streams, to the sliding window model. The second technique reduces the problem to skyline maintenance in the distance-time space and precomputes the future changes in the NN set. We analyze the performance of both algorithms and extend them to variations of NN search. Finally, we compare their efficiency through a comprehensive experimental evaluation. The skyline-based algorithm achieves lower CPU cost, at the expense of slightly larger space overhead.

Given a dataset P and a preference function f, a top-k query retrieves the k tuples in P with the highest scores according to f. Even though the problem is well-studied in conventional databases, the existing methods are inapplicable to highly dynamic environments involving numerous long-running queries. This paper studies continuous monitoring of top-k queries over a fixed-size window W of the most recent data. The window size can be expressed either in terms of the number of active tuples or time units. We propose a general methodology for top-k monitoring that restricts processing to the sub-domains of the workspace that influence the result of some query. To cope with high stream rates and provide fast answers in an on-line fashion, the data in W reside in main memory. The valid records are indexed by a grid structure, which also maintains book-keeping information. We present two processing techniques: the first one computes the new answer of a query whenever some of the current top-k points expire; the second one partially pre-computes the future changes in the result, achieving better running time at the expense of slightly higher space requirements. We analyze the performance of both algorithms and evaluate their efficiency through extensive experiments. Finally, we extend the proposed framework to other query types and a different data stream model.

Given a set of objects P and a query point q, a k nearest neighbor (k-NN) query retrieves the k objects in P that lie closest to q. Even though the problem is well-studied for static datasets, the traditional methods do not extend to highly dynamic environments where multiple continuous queries require real-time results, and both objects and queries receive frequent location updates. In this paper we propose conceptual partitioning (CPM), a comprehensive technique for the efficient monitoring of continuous NN queries. CPM achieves low running time by handling location updates only from objects that fall in the vicinity of some query (and ignoring the rest). It can be used with multiple, static or moving queries, and it does not make any assumptions about the object moving patterns. We analyze the performance of CPM and show that it outperforms the current state-of-the-art algorithms for all problem settings. Finally, we extend our framework to aggregate NN (ANN) queries, which monitor the data objects that minimize the aggregate distance with respect to a set of query points (e.g., the objects with the minimum sum of distances to all query points).

Despite the existence of obstacles in many database applications, traditional spatial query processing assumes that points in space are directly reachable and utilizes the Euclidean distance metric. In this paper, we study spatial queries in the presence of obstacles, where the obstructed distance between two points is defined as the length of the shortest path that connects them without crossing any obstacles. We propose efficient algorithms for the most important query types, namely, range search, nearest neighbours, e‐distance joins, closest pairs and distance semi‐joins, assuming that both data objects and obstacles are indexed by R‐trees. The effectiveness of the proposed solutions is verified through extensive experiments.

Assume a set of moving objects and a central server that monitors their positions over time, while processing continuous nearest neighbor queries from geographically distributed clients. In order to always report up-to-date results, the server could constantly obtain the most recent position of all objects. However, this naive solution requires the transmission of a large number of rapid data streams corresponding to location updates. Intuitively, current information is necessary only for objects that may influence some query result (i.e., they may be included in the nearest neighbor set of some client). Motivated by this observation, we present a threshold-based algorithm for the continuous monitoring of nearest neighbors that minimizes the communication overhead between the server and the data objects. The proposed method can be used with multiple, static, or moving queries, for any distance definition, and does not require additional knowledge (e.g., velocity vectors) besides object locations.

Given two spatial datasets P (e.g., facilities) and Q (queries), an aggregate nearest neighbor (ANN) query retrieves the point(s) of P with the smallest aggregate distance(s) to points in Q . Assuming, for example, n users at locations q 1 ,… q n , an ANN query outputs the facility p ∈ P that minimizes the sum of distances | pq i | for 1 ≤ i ≤ n that the users have to travel in order to meet there. Similarly, another ANN query may report the point p ∈ P that minimizes the maximum distance that any user has to travel, or the minimum distance from some user to his/her closest facility. If Q fits in memory and P is indexed by an R-tree, we develop algorithms for aggregate nearest neighbors that capture several versions of the problem, including weighted queries and incremental reporting of results. Then, we analyze their performance and propose cost models for query optimization. Finally, we extend our techniques for disk-resident queries and approximate ANN retrieval. The efficiency of the algorithms and the accuracy of the cost models are evaluated through extensive experiments with real and synthetic datasets.

Given two sets of points P and Q, a group nearest neighbor (GNN) query retrieves the point(s) of P with the smallest sum of distances to all points in Q. Consider, for instance, three users at locations q/sub 1/ q/sub 2/ and q/sub 3/ that want to find a meeting point (e.g., a restaurant); the corresponding query returns the data point p that minimizes the sum of Euclidean distances |pq/sub i/| for 1/spl les/i/spl les/3. Assuming that Q fits in memory and P is indexed by an R-tree, we propose several algorithms for finding the group nearest neighbors efficiently. As a second step, we extend our techniques for situations where Q cannot fit in memory, covering both indexed and nonindexed query points. An experimental evaluation identifies the best alternative based on the data and query properties.