Query Problem Research Articles

Optimal Location Queries in Road Networks

In this article, we study an optimal location query based on a road network. Specifically, given a road network containing clients and servers, an optimal location query finds a location on the road network such that when a new server is set up at this location, a certain cost function computed based on the clients and servers (including the new server) is optimized. Two types of cost functions, namely, MinMax and MaxSum, have been used for this query. The optimal location query problem with MinMax as the cost function is called the MinMax query, which finds a location for setting up a new server such that the maximum cost of a client being served by his/her closest server is minimized. The optimal location query problem with MaxSum as the cost function is called the MaxSum query, which finds a location for setting up a new server such that the sum of the weights of clients attracted by the new server is maximized. The MinMax query and the MaxSum query correspond to two types of optimal location query with the objectives defined from the clients' perspective and from the new server's perspective, respectively. Unfortunately, the existing solutions for the optimal query problem are not efficient. In this article, we propose an efficient algorithm, namely, MinMax-Alg ( MaxSum-Alg ), for the MinMax (MaxSum) query, which is based on a novel idea of nearest location component . We also discuss two extensions of the optimal location query, namely, the optimal multiple-location query and the optimal location query on a 3D road network. Extensive experiments were conducted, showing that our algorithms are faster than the state of the art by at least an order of magnitude on large real benchmark datasets. For example, in our largest real datasets, the state of the art ran for more than 10 (12) hours while our algorithm ran within 3 (2) minutes only for the MinMax (MaxSum) query, that is, our algorithm ran at least 200 (600) times faster than the state of the art.

Minimized-cost cube query on heterogeneous information networks

Data cube is the foundation of on-line analytical processing (OLAP), which can provide users with data views from different perspectives and granularities. Heterogeneous information networks consist of multiple types of nodes and edges which represent different semantic relations. With the rapid development of social networks and knowledge graphs, heterogeneous information networks have become increasingly popular. In heterogeneous information networks, cube is the set of aggregate graphs and cube query is required for supporting OLAP. The existing research mainly studies aggregate graph query on homogeneous networks, but only considers the attributes of nodes. To overcome these challenges, this paper investigates cube query problem on heterogeneous information networks. (1) A novel cube model for heterogeneous information networks is proposed, which captures both the attribute and structure semantics. (2) Because the total number of aggregate graphs is huge, computing and storing them cost plenty of time and storage. The problem of partial cube materialization on heterogeneous information networks is investigated. Given a fixed size of memory space, select a subset of aggregate graphs in cube, to minimize the computing cost of the whole cube. This optimization problem is proved to be NP-complete and there is no $$n^{1-{\epsilon }}$$n1-∈ approximation algorithm unless P $$=$$= NP. (3) A greedy algorithm is proposed for partial cube materialization based on two interesting dependencies between aggregate graphs, attribute dependence and path dependence. (4) Experiments on real world data sets show the cube definition is meaningful, and the partial cube materialization algorithm is efficient.

Encoding 2D range maximum queries

We consider the two-dimensional range maximum query (2D-RMQ) problem: given an array containing elements from an ordered set, encode the array so that the position of the maximum element in any specified range of rows and range of columns can be found efficiently. We focus on determining the effective entropy of 2D-RMQ, i.e., how many bits are needed to encode an array so that 2D-RMQ queries can be answered without accessing the array. We give tight upper and lower bounds on the expected effective entropy for the case when A contains independent identically-distributed random values, and give new upper and lower bounds for the case when the array contains few rows. The latter results improve upon the upper and lower bounds by Brodal et al. [4] . We also give some efficient data structures for 2D-RMQ whose space usage is close to the effective entropy.

Indexing a protein-protein interaction network expedites network alignment.

BackgroundNetwork query problem aligns a small query network with an arbitrarily large target network. The complexity of this problem grows exponentially with the number of nodes in the query network if confidence in the optimality of result is desired. Scaling this problem to large query and target networks remains to be a challenge.ResultsIn this article, we develop a novel index structure that dramatically reduces the cost of the network query problem. Our index structure maintains a small set of reference networks where each reference network is a small, carefully chosen subnetwork from the target network. Along with each reference, we also store all of its non-overlapping and statistically significant alignments with the target network. Given a query network, we first align the query with the reference networks. If the alignment with a reference network yields a sufficiently large score, we compute an upper-bound to the alignment score between the query and the target using the alignments of that reference and the target (which is stored in our index). If the upper-bound is large enough, we employ a second round of alignment between the query and the target by respecting the mapping found in the first alignment.Our experiments on protein-protein interaction networks demonstrate that our index achieves a significant speed-up in running time over the state-of-the-art methods such as ColT. The alignment subnetworks obtained by our method are also statistically significant. Finally, we observe that our method finds biologically and statistically significant alignments across multiple species.ConclusionsWe developed a reference network based indexing structure that accelerates network query and produces functionally and statistically significant results.

Geo-Social K-Cover Group Queries for Collaborative Spatial Computing

With the rapid development of location-aware mobile devices, ubiquitous Internet access and social computing technologies, lots of users’ personal information, such as location data and social data, has been readily accessible from various mobile platforms and online social networks. The convergence of these two types of data, known as geo-social data , has enabled collaborative spatial computing that explicitly combines both location and social factors to answer useful geo-social queries for either business or social good. In this paper, we study a new type of Geo-Social K-Cover Group (GSKCG) queries that, given a set of query points and a social network, retrieves a minimum user group in which each user is socially related to at least $k$ other users and the users’ associated regions (e.g., familiar regions or service regions) can jointly cover all the query points. Albeit its practical usefulness, the GSKCG query problem is NP-complete. We consequently explore a set of effective pruning strategies to derive an efficient algorithm for finding the optimal solution. Moreover, we design a novel index structure tailored to our problem to further accelerate query processing. Extensive experiments demonstrate that our algorithm achieves desirable performance on real-life datasets.

TN-82 — a relational database management system

Using the relational approach of database systems, the TN-82 has been developed and implemented on a minicomputer. The system consists of four subsystems which have relatively independent functions. These subsystems can be modified and extended, if it is necessary to obtain a new one, which adapts another practical problem with few costs. The system TN-82 is a structured system and controlled by hierarchical principles. The method based on module programming has been used. Data independence, data consistency, some problems of data security and integrity are guaranteed. Many blocks of information stored in the system can be unified. The system has been constructed in such a way that the redundant information is eliminated as much as possible. With a simple but powerful Vietnamese query manguage, a user is able to easily formulate his non-procedural queries. The query processing is based on implementing in the system of almost operations of relational algebra (such as selection, projection, join, union, difference, intersection). Some problems of query optimizing are resolved and applied in this system, such as analyzing and choosing the orders of executed operations, selecting access paths,… The system TN-82 can be easily realized on the other more powerful computers.

Symmetric searchable encryption with efficient range query using multi-layered linked chains

Searchable encryption is an encryption system which provides confidentiality of stored documents and usability of document search at the same time. Remote cloud storage is the most typical application for searchable encryption. By applying searchable encryption, inside attacks can be prevented fundamentally. Designing searchable encryption which supports various queries--for example, range query, conjunctive keyword query, etc.--is also important for convenience. In this paper, we focus on range query problem. Up to now, only a few results have been proposed, in which the main functions are constructed using public key cryptosystems. Therefore, the amount of computations for encryption and search is large. Furthermore, search time depends on the size of the entire database. We propose a new searchable encryption protocol which is based on a symmetric key encryption and linked chain structure instead of public key-based techniques. The main contribution of the protocol is reducing the computation cost for search remarkably. In reality, the amount of computations depends on the number of documents that are matched to a query instead of the size of the entire database.

Fast Algorithms for Local Similarity Queries in Two Sequences

In sequence comparison, finding local similarities in given strings is a very important well-known problem. In this work we introduce two local sequence similarity query problems, and present algorithms for them. Our algorithms use a data structure that supports constant time longest common extension queries. This data structure is created only once, and in time linear in the size of the input strings. After this step all subsequent local similarity queries can be answered very fast. Existing algorithms take significantly more time in answering these queries.

Privacy-Preserving and Content-ProtectingLocation Based Queries

This paper presents a solution to one of the location-based query problems. This problem is defined as follows: (i) a user wants to query a database of location data, known as Points Of Interest (POI), and does not want to reveal his/her location to the server due to privacy concerns; (ii) the owner of the location data, that is, the location server, does not want to simply distribute its data to all users. The location server desires to have some control over its data, since the data is its asset.A location based query solution that employs two protocols that enables a user to privately determine and acquire location data. The first step is for a user to privately determine his/her location using oblivious transfer on a public grid. Oblivious Transfer used to achieve a more secure solution for both parties.The second step involves a private information retrieval interaction that retrieves the record with high communication efficiency. The solution which is present is efficient and practical in many scenarios. This paper includes the results of a working prototype to illustrate the efficiency of our protocol.

Group nearest-neighbor queries in the [formula omitted] plane

Let P be a set of n points in the plane. The k-nearest-neighbor (abbreviated as k-NN) query problem is to preprocess P into a data structure that quickly reports k closest points in P for a query point q. This paper addresses a generalization of the k-NN query problem to a query set Q of points, namely, the group k-nearest-neighbor query problem, in the L1 plane. More precisely, a query is assigned with a set Q of at most m points and a positive integer k with k≤n, and the distance between a point p of P and a query set Q is defined as the sum of L1 distances from p to all q∈Q. The maximum number m of query points Q is assumed to be known in advance and to be at most n. In this paper, we propose two algorithms, one based on the range tree and the other based on a data structure for segment dragging queries, and obtain the following complexity bounds: (1) a group k-NN query can be handled in O(Tminlog⁡n+(k+m2)(log⁡log⁡n+log⁡m)) time after preprocessing P using O(m2nlog2⁡n) space, where Tmin=min⁡{k+m,m2}, or (2) a group k-NN query can be handled in O((k+m)log2⁡n+m2(logϵ⁡n+log⁡m)) time after preprocessing P using O(m2n) space, where ϵ>0 is an arbitrarily small constant. We also show that our approach can be applied to the weighted group k-nearest-neighbor query problem and the group k-farthest-neighbor query problem.

An Efficient Index Structure for Range Count Query Based on Improved B-tree

With the development of many new technologies, the data grows much faster than before, and most of the data are stored in database. When the data amount in database is huge, it is needed to create appropriate index to keep the performance of database applications. This paper proposes a new index structure, namely MCRC-tree, to solve complicated range count query problem efficiently. Multiple experiments show that the performance of MCRC-tree is similar to that of traditional B-tree, to be specific, MCRC-tree can solve any range count query in O(������ ����) I/Os, so is the complexity of insert and delete operations, here C means the disk page size, N is the total data amount.

On hardness of several string indexing problems

Let D={d1,d2,…,dD} be a collection of D string documents of n characters in total. The two-pattern matching problems ask to index D for answering the following queries efficiently.•Report/count the unique documents containing P1 and P2.•Report/count the unique documents containing P1, but not P2.Here P1 and P2 represent input patterns of length p1 and p2 respectively. Linear space data structures with O(p1+p2+nklogO(1)⁡n) query cost are already known for the reporting version, where k represents the output size. For the counting version (i.e., report the value k), a simple linear-space index with O(p1+p2+n) query cost can be constructed in O(n3/2) time. However, it is still not known if these are the best possible bounds for these problems. In this paper, we show a strong connection between these string indexing problems and the boolean matrix multiplication problem. Based on this, we argue that these results cannot be improved significantly using purely combinatorial techniques. We also provide an improved upper bound for a related problem known as common colors query problem.

Sketching distributed sliding-window data streams

While traditional data management systems focus on evaluating single, ad hoc queries over static data sets in a centralized setting, several emerging applications require (possibly, continuous) answers to queries on dynamic data that is widely distributed and constantly updated. Furthermore, such query answers often need to discount data that is "stale" and operate solely on a sliding window of recent data arrivals (e.g., data updates occurring over the last 24 h). Such distributed data streaming applications mandate novel algorithmic solutions that are both time and space efficient (to manage high-speed data streams) and also communication efficient (to deal with physical data distribution). In this paper, we consider the problem of complex query answering over distributed, high-dimensional data streams in the sliding-window model. We introduce a novel sketching technique (termed ECM-sketch) that allows effective summarization of streaming data over both time-based and count-based sliding windows with probabilistic accuracy guarantees. Our sketch structure enables point, as well as inner product, queries and can be employed to address a broad range of problems, such as maintaining frequency statistics, finding heavy hitters, and computing quantiles in the sliding-window model. Focusing on distributed environments, we demonstrate how ECM-sketches of individual, local streams can be composed to generate a (low-error) ECM-sketch summary of the order-preserving merging of all streams; furthermore, we show how ECM-sketches can be exploited for continuous monitoring of sliding-window queries over distributed streams. Our extensive experimental study with two real-life data sets validates our theoretical claims and verifies the effectiveness of our techniques. To the best of our knowledge, ours is the first work to address efficient, guaranteed-error complex query answering over distributed data streams in the sliding-window model.

Low space data structures for geometric range mode query

Let S be a set of n points in d dimensions such that each point is assigned a color. Given a query range Q=[a1,b1]×[a2,b2]×…×[ad,bd], the geometric range mode query problem asks to report the most frequent color (i.e., a mode) of the multiset of colors corresponding to points in S∩Q. When d=1, Chan et al. (2012) [1] gave a data structure that requires O(n+(n/Δ)2/w) words and supports range mode queries in O(Δ) time for any Δ≥1, where w=Ω(log⁡n) is the word size. Chan et al. also proposed a data structures for higher dimensions (i.e., d≥2) with O(sn+(n/Δ)2d) words and O(Δ⋅tn) query time, where sn and tn denote the space and query time of a data structure that supports orthogonal range counting queries on the set S. In this paper we show that the space can be improved without any increase to the query time, by presenting an O(sn+(n/Δ)2d/w)-word data structure that supports orthogonal range mode queries on a set of n points in d dimensions in O(Δ⋅tn) time, for any Δ≥1. When d=1, these space and query time costs match those achieved by the current best known one-dimensional data structure.

MATHEMATICAL MODEL TRANSLATION OF NATURAL LANGUAGE QUERIES USING ABSTRACT QUERY LANGUAGE ABSTRACT

This article describes construction and investigation the mathematical model of natural query language translation with using an abstract query language. Abstract query language allows separating the problem of the processing natural language texts with using the linguistic knowledge from a problem of generation target query. Separation these tasks enables the construction modular natural language interface, which will be determined the same time translation algorithms to different sources of structured data in different target language queries using subsets of linguistic knowledge bases describing the desired subject area. In the proposed mathematical model is given a way to describe the concepts and relationships between them. With the help of concepts and relations between them, determined the linguistic knowledge base is used in the algorithm of translation. So, conversion of NL-queries to the abstract queries is reduced to sequential application of constructs for the purpose of separation from NL-queries of concepts of data domain, their components and the relations in between and to display of the selected concepts to a data model of the target structured data source.

Privacy-Preserving Top-k Spatial Keyword Queries in Untrusted Cloud Environments

With the rapid development of location-based services in mobile Internet, spatial keyword queries have been widely employed in various real-life applications in recent years. To realize the great flexibility and cost savings, more and more data owners are motivated to outsource their spatio-textual data services to the cloud. However, directly outsourcing such services to the untrusted cloud may arise serious privacy concerns. In this paper, we study the privacy-preserving top- $k$ spatial keyword query problem in untrusted cloud environments. Existing studies primarily focus on the design of privacy-preserving schemes for either spatial or keyword queries, and they cannot be applied to solve the privacy-preserving spatial keyword query problem. To address this problem, we present a novel privacy-preserving top- $k$ spatial keyword query scheme. In particular, we build an encrypted tree index to facilitate privacy-preserving top- $k$ spatial keyword queries, where spatial and textual data are encrypted in a unified way. To search with the encrypted tree index, we propose two effective techniques for the similarity computations between queries and tree nodes under encryption. To improve query performance on large-scale spatio-textual data, we further propose a keyword-based secure pruning method. Thorough analysis shows the validity and security of our scheme. Extensive experimental results on real datasets demonstrate our scheme achieves high efficiency and good scalability.

Specification and Enforcement of the General User Authorization Query Problem in Role Based Access Control System

Specification and Enforcement of the General User Authorization Query Problem in Role Based Access Control System

Ontology-Based Data Access

Ontology-based data access is concerned with querying incomplete data sources in the presence of domain-specific knowledge provided by an ontology. A central notion in this setting is that of an ontology-mediated query , which is a database query coupled with an ontology. In this article, we study several classes of ontology-mediated queries, where the database queries are given as some form of conjunctive query and the ontologies are formulated in description logics or other relevant fragments of first-order logic, such as the guarded fragment and the unary negation fragment. The contributions of the article are threefold. First, we show that popular ontology-mediated query languages have the same expressive power as natural fragments of disjunctive datalog, and we study the relative succinctness of ontology-mediated queries and disjunctive datalog queries. Second, we establish intimate connections between ontology-mediated queries and constraint satisfaction problems (CSPs) and their logical generalization, MMSNP formulas. Third, we exploit these connections to obtain new results regarding: (i) first-order rewritability and datalog rewritability of ontology-mediated queries; (ii) P/NP dichotomies for ontology-mediated queries; and (iii) the query containment problem for ontology-mediated queries.

Partial Information Network Queries

We study the Partial Information Network Query (PINQ) problem, which generalizes two problems that often arise in bioinformatics: the Alignment Network Query (ANQ) problem and the Topology-Free Network Query (TFNQ) problem. In both ANQ and TFNQ we have a pattern P and a graph H, and we seek a subgraph of H that resemblesP. ANQ requires knowing the topology of P, while TFNQ ignores it. PINQ fits the scenario where partial information is available on the topology of P. Our main result is a parameterized algorithm that handles inputs for PINQ in which P is a set of trees. This algorithm significantly improves the best known O⁎ running time in solving TFNQ. We also improve the best known O⁎ running times in solving two special cases of ANQ.

A General Technique for Top-<inline-formula><tex-math>$k$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="janardan-ieq1-2316807.gif"/></alternatives></inline-formula> Geometric Intersection Query Problems

In a top-k Geometric Intersection Query (top-k GIQ) problem, a set of n weighted, geometric objects in Rd is to be preprocessed into a compact data structure so that for any query geometric object, q, and integer k > 0, the k largest-weight objects intersected by q can be reported efficiently. While the top-k problem has been studied extensively for non-geometric problems (e.g., recommender systems), the geometric version has received little attention. This paper gives a general technique to solve any top-k GIQ problem efficiently. The technique relies only on the availability of an efficient solution for the underlying (non-top-k) GIQ problem, which is often the case. Using this, asymptotically efficient solutions are derived for several top-k GIQ problems, including top-k orthogonal and circular range search, point enclosure search, halfspace range search, etc. Implementations of some of these solutions, using practical data structures, show that they are quite efficient in practice. This paper also does a formal investigation of the hardness of the top-k GIQ problem, which reveals interesting connections between the top-k GIQ problem and the underlying (non-top-k) GIQ problem.