Query Problem Research Articles

An (Almost) Optimal Solution for Orthogonal Point Enclosure Query in ℝ3

The orthogonal point enclosure query (OPEQ) problem is a fundamental problem in the context of data management for modeling user preferences. Formally, preprocess a set S of n axis-aligned boxes (possibly overlapping) in ℝd into a data structure so that the boxes in S containing a given query point q can be reported efficiently. In the pointer machine model, optimal solutions for the OPEQ in ℝ1 and ℝ2 were discovered in the 1980s: linear-space data structures that can answer the query in O(log n + k) query time, where k is the number of boxes reported. However, for the past three decades, an optimal solution in ℝ3 has been elusive. In this work, we obtain the first data structure that almost optimally solves the OPEQ in ℝ3 in the pointer machine model: an O(n log* n)-space data structure with O(log2 n · log log n + k) query time. Here, log* n is the iterated logarithm of n. This almost matches the lower-bound, which states that any data structure that occupies O(n) space requires Ω(log2 n + k) time to answer an OPEQ in ℝ3. Finally, we also obtain the best known bounds for the OPEQ in higher dimensions (d ≥ 4).

Efficient Range Queries for Large-Scale Sensor-Augmented RFID Systems

This paper studies the practically important problem of range query for sensor-augmented RFID systems, which is to classify the target tags according to the ranges specified by the user. The existing RFID protocols that seem to address this problem suffer from either low time-efficiency or the information corruption issue. To overcome their limitations, we first propose a basic classification protocol called Range Query (RQ) , in which each tag pseudo-randomly chooses a slot from the time frame and uses the ON-OFF Keying modulation to reply its range identifier. Then, RQ employs a collaborative decoding method to extract the tag range information from singleton and even collision slots. The numerical results reveal that the number of queried ranges significantly affects the performance of RQ . To optimize the number of queried ranges, we further propose the PartitionM (ii) it is immune to the interference from unexpected tags, and does not suffer information corruption issue. We use USRP and WISP tags to conduct a set of experiments, which demonstrate the feasibility of RQ + PM . Extensive simulation results reveal that RQ + PM can ensure 100% query accuracy, and reduce the time cost as much as 40% when comparing with the state-of-the-art protocols.

Diversified top-k search with relaxed graph simulation

Graph pattern matching has been widely used in a broad spectrum of real-world applications, and it has been the subject of several investigations, mainly of its importance and use. In this context, different models along with their appropriate algorithms have been proposed. However, in addition to the excessive processing costs, most of the existing models suffer from the failing query problem due to their limitations on finding meaningful matches. Also, in some scenarios, the number of matches may be enormous, making the inspection a daunting task. In this work, we introduce a new model for graph pattern matching, called relaxed graph simulation (RGS), allowing the relaxation of queries to identify more significant matches and to avoid the empty-set answer problem. We then formalize and study the top-k matching problem based on two function classes, relevance and diversity, for ranking the matches with respect to the proposed model. We also formalize and investigate the diversified top-k matching problem, and we propose a diversification function to balance relevance and diversity. Nonetheless, we provide efficient algorithms based on optimization strategies to compute the top-k and the diversified top-k matches according to the RGS model. Our experimental results, on four real datasets, demonstrate both the effectiveness and the efficiency of the proposed approaches.

Measuring similarity for query in geometric data

One of the most important steps which is used in every data mining projects is searching an object or some similar objects in a data set. For geometric data, there are some methods that measure the difference between two objects. In recent years, researchers have focused on these types of metrics and used them in different applications (e.g., shape matching, machine vision, map generation, etc.). The query problem in these kinds of applications is more complicated when we have big data. In this paper, a new metric is presented which works efficiently when the geometric objects are in discrete form (e.g., polygon or chain). The presented method is important from a theoretical point of view, and its differences with other similar metrics are discussed in this paper.

Multiple-user closest keyword-set querying in road networks

Location-based group queries have attracted increasing attention due to the prevalence of location-based services (LBS) and location-based social networks (LBSN). An important and practical application in these queries is the multiple-user closest keyword-set (MCKS) query that aims to search a set of Points of Interest (POIs) for multiple users in road networks. These POIs cover the query keyword-set, are close to the locations of multiple users, and are close to each other. This problem has been proved to be NP-hard. Unfortunately, existing solutions cannot handle this query efficiently and effectively. Specifically, the existing exact approach does not scale well with the network sizes and the existing approximation approaches, though scalable, have large error bounds. To address the above issues, a series of enhanced algorithms are proposed for the MCKS query problem in this paper. Specifically, a 3-approximation feasible result search algorithm is first proposed. Then, using the cost of the result returned by this algorithm as an upper bound, we present an efficient exact algorithm and an approximation algorithm with better performance guarantee. The exact algorithm is designed based on a set of efficient optimizations. The approximation algorithm improves the best-known approximation ratio from 157 to 1.5. Extensive performance studies with two real datasets demonstrate the effectiveness and efficiency of our proposed algorithms, which outperform existing algorithms significantly.

FJLT-FLSH: More Efficient Fly Locality-Sensitive Hashing Algorithm via FJLT for WMSN IoT Search

Wireless multimedia sensor networks (WMSNs) have been widely used in environmental monitoring, intelligent transportation, and other scenarios; however, their data have high-dimensional, large-scale, and multitype properties, as well as other characteristics. It is technically difficult to construct an appropriate index structure and query strategy while we perform a highly accurate search. This paper proposes a novel locality-sensitive hashing (LSH) fast Johnson–Lindenstrauss transform (FJLT)-fly locality-sensitive hashing (FLSH) algorithm for WMSN Internet of Things search. In this method, the projection method of FJLT and the winner-takes-all feature selection strategy in the fruity FLSH are considered. The method provides a new solution for the nearest neighbor search of high-dimensional data. We also discuss the distance-keeping property of our algorithm, and prove theoretically that the method proposed in this paper has better distance keeping performance than the traditional dimensionality reduction method. The experimental results show that the proposed algorithm has better generalization, accuracy of the search results, and time efficiency when using the Drosophila olfactory nerve to simulate the LSH process. This method effectively solves the problem of the approximate neighbor query of high-dimensional big data and can be effectively applied to search application on WMSN system.

A Conflict Detection Method for Ellipsoidal Safety Regions

A novel pairwise comparison algorithm that determines whether a pair of ellipsoids is overlapping, touching, or separated by exploiting two new Mahalanobis distance conditions is presented. These conditions are filtering steps prior to a previously proposed method that transforms the intersection query problem into a least squares minimization over a sphere. The algorithm introduced here is shown to be more computationally efficient than previous methods, which either solve the least squares minimization over a sphere or check the signs of the roots of a fourth order polynomial. The proposed method may be applied in cases when an ellipsoid is an appropriate safety region. The resulting algorithm is useful in applications including, but not limited to, collision detection and avoidance.

Landmark community approximate optimal path query with pure linguistic attributes

The optimal path problem of graph data is a classical problem, but with the continuous development of information technology, the linguistic attributes of path descriptions are becoming more and more abundant. The nature of these attributes has uncertainty feature, which makes the choice of optimal path more difficult. Therefore, in order to solve this problem, this paper takes the path of large-scale graph and the uncertainty of linguistic attributes and the inconsistency of evaluation scale described as the research object and uses LWAA and landmark community technology to effectively solve the query problem of approximate optimal path. The efficiency of the algorithm is also verified by experiments.

Quickest Visibility Queries in Polygonal Domains

Let s be a point in a polygonal domain $${\mathcal {P}}$$ of $$h-1$$ holes and n vertices. We consider a quickest visibility query problem. Given a query point q in $${\mathcal {P}}$$ , the goal is to find a shortest path in $${\mathcal {P}}$$ to move from s to seeq as quickly as possible. Previously, Arkin et al. (SoCG 2015) built a data structure of size $$O(n^22^{\alpha (n)}\log n)$$ that can answer each query in $$O(K\log ^2 n)$$ time, where $$\alpha (n)$$ is the inverse Ackermann function and K is the size of the visibility polygon of q in $${\mathcal {P}}$$ (and K can be $$\varTheta (n)$$ in the worst case). In this paper, we present a new data structure of size $$O(n\log h + h^2)$$ that can answer each query in $$O(h\log h\log n)$$ time. Our result improves the previous work when h is relatively small. In particular, if h is a constant, then our result even matches the best result for the simple polygon case (i.e., $$h=1$$ ), which is optimal. As a by-product, we also have a new algorithm for a shortest-path-to-segment query problem. Given a query line segment $$\tau $$ in $${\mathcal {P}}$$ , the query seeks a shortest path from s to all points of $$\tau $$ . Previously, Arkin et al. gave a data structure of size $$O(n^22^{\alpha (n)}\log n)$$ that can answer each query in $$O(\log ^2 n)$$ time, and another data structure of size $$O(n^3\log n)$$ with $$O(\log n)$$ query time. We present a data structure of size O(n) with query time $$O\big (h\log \frac{n}{h}\big )$$ , which also favors small values of h and is optimal when $$h=O(1)$$ .

Parallel-Correctness and Containment for Conjunctive Queries with Union and Negation

Single-round multiway join algorithms first reshuffle data over many servers and then evaluate the query at hand in a parallel and communication-free way. A key question is whether a given distribution policy for the reshuffle is adequate for computing a given query, also referred to as parallel-correctness. This article extends the study of the complexity of parallel-correctness and its constituents, parallel-soundness and parallel-completeness, to unions of conjunctive queries with negation. As a by-product, it is shown that the containment problem for conjunctive queries with negation is coNEXPTIME-complete.

Verifiable Stream Computation and Arthur--Merlin Communication

In the setting of streaming interactive proofs (SIPs), a client (verifier) needs to compute a given function on a massive stream of data, arriving online, but is unable to store even a small fraction of the data. It outsources the processing to a third party service (prover) but is unwilling to blindly trust answers returned by this service. Thus, the service cannot simply supply the desired answer; it must convince the verifier of its correctness via a short interaction after the stream has been seen. In this work we study “barely interactive” SIPs. Specifically, we show that one or two rounds of interaction suffice to solve several query problems---including index, median, nearest neighbor search, pattern matching, and range counting---with polylogarithmic space and communication costs. Such efficiency with $O(1)$ rounds of interaction was thought to be impossible based on previous work. On the other hand, we initiate a formal study of the limitations of constant-round SIPs by introducing a new hierarchy of communication models called online interactive proofs (OIPs). The online nature of these models is analogous to the streaming restriction placed upon the verifier in a SIP. We give upper and lower bounds that (1) characterize, up to quadratic blowups, every finite level of the OIP hierarchy in terms of other well-known communication complexity classes, (2) separate the first four levels of the hierarchy, and (3) reveal that the hierarchy collapses to the fourth level. Our study of OIPs reveals marked contrasts and some parallels with the classic Turing machine theory of interactive proofs, establishes limits on the power of existing techniques for developing constant-round SIPs, and provides a new characterization of (nononline) Arthur--Merlin communication in terms of an online model.

Lower Bounds for Data Structures with Space Close to Maximum Imply Circuit Lower Bounds

$ \newcommand{\zo}{\{0, 1\}} $ Let $f:\zo^{n}\to\zo^{m}$ be a function computable by a circuit with unbounded fan-in, arbitrary gates, $w$ wires and depth $d$. With a very simple argument we show that the $m$-query problem corresponding to $f$ has data structures with space $s=n+r$ and time $(w/r)^{d}$, for any $r$. As a consequence, in the setting where $s$ is close to $m$ a slight improvement on the state of existing data-structure lower bounds would solve long-standing problems in circuit complexity. We also use this connection to obtain a data structure for error-correcting codes which nearly matches the 2007 lower bound by Gál and Miltersen. This data structure can also be made dynamic. Finally we give a problem that requires at least $3$ bit probes for $m=n^{O(1)}$ and even $s=m/2-1$. Independent work by Dvir, Golovnev, and Weinstein (2018) and by Corrigan-Gibbs and Kogan (2018) give incomparable connections between data-structure and other types of lower bounds.

Platform-Independent Robust Query Processing

To address the classical selectivity estimation problem for OLAP queries in relational databases, a radically different approach called PlanBouquet was recently proposed in [1] , wherein the estimation process is completely abandoned and replaced with a calibrated discovery mechanism. The beneficial outcome of this new construction is that provable guarantees on worst-case performance, measured as Maximum Sub-Optimality ( MSO ), are obtained thereby facilitating robust query processing. The PlanBouquet formulation suffers, however, from a systemic drawback—the MSO bound is a function of not only the query, but also the optimizer's behavioral profile over the underlying database platform. As a result, there are adverse consequences: (i) the bound value becomes highly variable, depending on the specifics of the current operating environment, and (ii) it becomes infeasible to compute the value without substantial investments in preprocessing overheads. In this paper, we first present SpillBound , a new query processing algorithm that retains the core strength of the PlanBouquet discovery process, but reduces the bound dependency to only the query. It does so by incorporating plan termination and selectivity monitoring mechanisms in the database engine. Specifically, SpillBound delivers a worst-case multiplicative bound, of $D^2+3D$ , where $D$ is simply the number of error-prone predicates in the user query. Consequently, the bound value becomes independent of the optimizer and the database platform, and the guarantee can be issued simply by query inspection. We go on to prove that SpillBound is within an $O(D)$ factor of the best possible deterministic selectivity discovery algorithm in its class. We next devise techniques to bridge this quadratic-to-linear MSO gap by introducing the notion of contour alignment , a characterization of the nature of plan structures along the boundaries of the selectivity space. Specifically, we propose a variant of SpillBound , called AlignedBound , which exploits the alignment property and provides a guarantee in the range $\mathbf {[2D+2,D^2+3D]}$ . Finally, a detailed empirical evaluation over the standard decision-support benchmarks indicates that: (i) SpillBound provides markedly superior performance w.r.t. MSO as compared to PlanBouquet , and (ii) AlignedBound provides additional benefits for query instances that are challenging for SpillBound , often coming close to the ideal of MSO linearity in $D$ . From an absolute perspective, AlignedBound evaluates virtually all the benchmark queries considered in our study with MSO of around 10 or lesser. Therefore, in an overall sense, SpillBound and AlignedBound offer a substantive step forward in the long-standing quest for robust query processing.

A Novel Index Method for K Nearest Object Query over Time‐Dependent Road Networks

K nearest neighbor (kNN) search is an important problem in location-based services (LBS) and has been well studied on static road networks. However, in real world, road networks are often time‐dependent; i.e., the time for traveling through a road always changes over time. Most existing methods for kNN query build various indexes maintaining the shortest distances for some pairs of vertices on static road networks. Unfortunately, these methods cannot be used for the time‐dependent road networks because the shortest distances always change over time. To address the problem of kNN query on time‐dependent road networks, we propose a novel voronoi‐based index in this paper. Furthermore, we propose a novel balanced tree, named ‐, which is a secondary level index on voronoi‐based index to make our querying algorithm more efficient. Moreover, we propose an algorithm for preprocessing time‐dependent road networks such that the waiting time is not necessary to be considered. We confirm the efficiency of our method through experiments on real‐life datasets.

Who Should Be Invited to My Party: A Size-Constrained k-Core Problem in Social Networks

In this paper, we investigate the problem of a size-constrained k-core group query (SCCGQ) in social networks, taking both user closeness and network topology into consideration. More specifically, SCCGQ intends to find a group of h users that has the highest social closeness while being a k-core. SCCGQ can be widely applied to event planning, task assignment, social analysis, and many other fields. In contrast to existing work on the k-core detection problem, which aims to find a k-core in a social network, SCCGQ not only focuses on k-core detection but also takes size constraints into consideration. Although the conventional k-core detection problem can be solved in linear time, SCCGQ has a higher complexity. To solve the problem of SCCGQ, we propose a Blast Scatter (BS) algorithm, which appoints the query node as the center to begin outward expansions via breadth search. In each outward expansion, BS finds a new center through a greedy strategy and then selects multiple neighbors of the center. To speed up the BS algorithm, we propose an advanced search algorithm, called Bounded Extension (BE). Specifically, BE combines an effective social distance pruning strategy and a tight upper bound of social closeness to prune the search space considerably. In addition, we propose an offline social-aware index to accelerate the query processing. Finally, our experimental results demonstrate the efficiency and effectiveness of our proposed algorithms on large real-world social networks.

How Bob in Quantum Private Query Protocol Gets the Element?

Quantum private query (QPQ) requires that the database holder Bob knows nothing about his client Alice, including the index she provides and the element she obtains. However, on some occasion, Bob wants to know which element he has revealed to Alice. Therefore, we raise a symmetric quantum private query (SQPQ) problem in this paper. SQPQ can guarantee that Alice shares the real element with Bob and also partially protect the privacy of Alice's index. Some necessary conditions are need to satisfy to implement SQPQ. We prove that Alice must provide some extra information to enable Bob to know the element. Then, we define the term “absolutely secure”, which is a security notion stronger than cheat sensitive, and prove that “absolutely secure”SQPQ is impossible. In addition, we raise a cheat sensitive scheme three-databases-detection to implement SQPQ protocol. Finally, we construct a reduction from SQPQ to quantum bit commitment (QBC) to clarify that SQPQ is a problem more difficult than QBC.

Improved Parameterized Algorithms for Network Query Problems

In the Partial Information Network Query (PINQ) problem, we are given a host graph H, and a pattern $${\mathcal {P}}$$P whose topology is partially known. We seek a connected subgraph of H that resembles$${\mathcal {P}}$$P. PINQ is a generalization of Subgraph Isomorphism, where the topology of $${\mathcal {P}}$$P is known, and Graph Motif, where the topology of $${\mathcal {P}}$$P is unknown. This generalization addresses the major challenge of analyzing biological networks in the absence of certain topological data. In this paper, we use a non-standard hybridization of algebraic and combinatorial tools to develop an exact parameterized algorithm as well as an FPT-approximation scheme for PINQ.

Answering the Min-Cost Quality-Aware Query on Multi-Sources in Sensor-Cloud Systems.

In sensor-based systems, the data of an object is often provided by multiple sources. Since the data quality of these sources might be different, when querying the observations, it is necessary to carefully select the sources to make sure that high quality data is accessed. A solution is to perform a quality evaluation in the cloud and select a set of high-quality, low-cost data sources (i.e., sensors or small sensor networks) that can answer queries. This paper studies the problem of min-cost quality-aware query which aims to find high quality results from multi-sources with the minimized cost. The measurement of the query results is provided, and two methods for answering min-cost quality-aware query are proposed. How to get a reasonable parameter setting is also discussed. Experiments on real-life data verify that the proposed techniques are efficient and effective.

Approximate Order-Sensitive k-NN Queries over Correlated High-Dimensional Data

The k Nearest Neighbor ( k -NN) query has been gaining more importance in extensive applications involving information retrieval, data mining, and databases. Specifically, in order to trade off accuracy for efficiency, approximate solutions for the k -NN query are extensively explored. However, the precision is usually order-insensitive, which is defined on the result set instead of the result sequence. In many situations, it cannot reasonably reflect the query result quality. In this paper, we focus on the approximate k -NN query problem with the order-sensitive precision requirement and propose a novel scheme based on the projection-filter-refinement framework. Basically, we adopt PCA to project the high-dimensional data objects into the low-dimensional space. Then, a filter condition is inferred to execute efficient pruning over the projected data. In addition, an index strategy named OR-tree is proposed to reduce the I/O cost. The extensive experiments based on several real-world data sets and a synthetic data set are conducted to verify the effectiveness and efficiency of the proposed solution. Compared to the state-of-the-art methods, our method can support order-sensitive k -NN queries with higher result precision while retaining satisfactory CPU and I/O efficiency.

Cooperative treatment of failing queries over uncertain databases: a matrix-computation-based approach

A large number of applications such as sensor networks, RFID-based monitoring systems, mobile object management and location-based services manage data pervaded with uncertainty. Usually, users wish/prefer high quality results (i.e. with highest certainty) when they pose queries with strict conditions over these data. However, as they may not be clear about the contents of the databases that contain such data, these queries may be failing i.e., they may return no result or results that do not satisfy the expected level of certainty. In this case, users may try to change manually the query conditions to obtain approximate answers. Due to the exponential combination number of query conditions, this procedure results in a time-consuming and frustrating task. In this paper, we address the failing queries problem by proposing an approach that identifies the query parts, called Minimal Failing Subqueries (MFSs), that are responsible for its failure. Thanks to these MFSs, interactive and automatic approaches can be set up to help the user reformulating her/his query. We also compute, in the same time, a set of Maximal Succeeding Subqueries (XSSs) that represents a list of non failing queries with a maximal number of predicates of the initial query. The results of these XSSs constitute good alternative answers that can be returned to the user instead of an empty result. To demonstrate the efficiency and the effectiveness of our proposal, a set of experiments have been conducted with synthetic and real datasets. A comparison with baseline and related work approaches shows the interest of our proposal.