Path Queries Research Articles

On-the-Fly Nearest-Shelter Computation in Event-Dependent Spatial Networks in Disasters

Numerous approaches have been proposed to solve shortest path query problems (SPQPs) in either static or time-dependent spatial networks; however, these approaches are neither appropriately nor efficiently used to find the nearest shelter with fastest paths in disaster evacuations. In disasters, segments of a path computed and saved as the fastest might become impassable. The nearest shelter differs for people depending on their locations and can also change on the basis of an unpredictable and highly dynamic edge cost (e.g., maximum passable vehicle speed), which is influenced by disaster events. To solve this problem, this paper proposes a dynamic network model, which is called an event-dependent network, to represent a spatial network in a disaster. Effective approaches using multiple algorithms are proposed for on-the-fly computation of the nearest shelter and fastest paths in a disaster. A distributed system consisting of a server and multiple mobile clients using our approaches is presented for navigating the fastest paths for people to evacuate a disaster area. Real-world maps, such as a map of California, were used in our experiments. The results revealed that our approaches require less than 2 ms to find a new nearest shelter and its fastest paths, which is faster than other approaches for solving the fastest-path problem.

The Dual Half-Edge—A Topological Primal/Dual Data Structure and Construction Operators for Modelling and Manipulating Cell Complexes

There is an increasing need for building models that permit interior navigation, e.g., for escape route analysis. This paper presents a non-manifold Computer-Aided Design (CAD) data structure, the dual half-edge based on the Poincaré duality that expresses both the geometric representations of individual rooms and their topological relationships. Volumes and faces are expressed as vertices and edges respectively in the dual space, permitting a model just based on the storage of primal and dual vertices and edges. Attributes may be attached to all of these entities permitting, for example, shortest path queries between specified rooms, or to the exterior. Storage costs are shown to be comparable to other non-manifold models, and construction with local Euler-type operators is demonstrated with two large university buildings. This is intended to enhance current developments in 3D Geographic Information Systems for interior and exterior city modelling.

Associating ω-automata to path queries on Webs of Linked Data

The emergence of semantically enriched data sets along with the elegant way they are interconnected through the concept of Linked Data has drawn the attention of various scientific branches. The volume and the quality of information that lie inside the Linked Data, along with the capabilities they offer, make them ideal candidate to mine and extract valuable material, especially in field with great humanitarian importance, such as biology, medicine and social networks. As we show in this work, there are queries that cannot be expressed neither by standard SPARQL nor its expansion with regular path queries.These queries assume that the Web of Linked Data can be infinite and therefore classic queries expressed in terms of SPARQL need to be revisited. We propose a novel method associating a variant of automata that read infinite inputs, namely the ω-automata, to each query on this infinite structure. We call these queries ω-regular path queries due to the fact that they use ω-regular expressions. These expressions are of the form a|(ab)ω. We present our approach through examples and figures, mainly inspired from systems related to biological functions and processes, providing indicative queries.

An Energy-Efficient Skyline Query for Massively Multidimensional Sensing Data.

Cyber physical systems (CPS) sense the environment based on wireless sensor networks. The sensing data of such systems present the characteristics of massiveness and multi-dimensionality. As one of the major monitoring methods used in in safe production monitoring and disaster early-warning applications, skyline query algorithms are extensively adopted for multiple-objective decision analysis of these sensing data. With the expansion of network sizes, the amount of sensing data increases sharply. Then, how to improve the query efficiency of skyline query algorithms and reduce the transmission energy consumption become pressing and difficult to accomplish issues. Therefore, this paper proposes a new energy-efficient skyline query method for massively multidimensional sensing data. First, the method uses a node cut strategy to dynamically generate filtering tuples with little computational overhead when collecting query results instead of issuing queries with filters. It can judge the domination relationship among different nodes, remove the detected data sets of dominated nodes that are irrelevant to the query, modify the query path dynamically, and reduce the data comparison and computational overhead. The efficient dynamic filter generated by this strategy uses little non-skyline data transmission in the network, and the transmission distance is very short. Second, our method also employs the tuple-cutting strategy inside the node and generates the local cutting tuples by the sub-tree with the node itself as the root node, which will be used to cut the detected data within the nodes of the sub-tree. Therefore, it can further control the non-skyline data uploading. A large number of experimental results show that our method can quickly return an overview of the monitored area and reduce the communication overhead. Additionally, it can shorten the response time and improve the efficiency of the query.

Approximation and Relaxation of Semantic Web Path Queries

Given the heterogeneity of complex graph data on the web, such as RDF linked data, it is likely that a user wishing to query such data will lack full knowledge of the structure of the data and of its irregularities. Hence, providing flexible querying capabilities that assist users in formulating their information seeking requirements is highly desirable. In this paper we undertake a detailed theoretical investigation of query approximation, query relaxation, and their combination, for this purpose. The query language we adopt comprises conjunctions of regular path queries, thus encompassing recent extensions to SPARQL to allow for querying paths in graphs using regular expressions (SPARQL 1.1). To this language we add standard notions of query approximation based on edit distance, as well as query relaxation based on RDFS inference rules. We show how both of these notions can be integrated into a single theoretical framework and we provide incremental evaluation algorithms that run in polynomial time in the size of the query and the data, returning answers in ranked order of their `distance' from the original query. We also combine for the rst time these two disparate notions into a single `ex' operation that simultaneously applies both approximation and relaxation to a query conjunct, providing even greater flexibility for users, but still retaining polynomial time evaluation complexity and the ability to return query answers in ranked order.

Semantic Acyclicity on Graph Databases

It is known that unions of acyclic conjunctive queries (CQs) can be evaluated in linear time, as opposed to arbitrary CQs, for which the evaluation problem is NP-complete. It follows from techniques in the area of constraint-satisfaction problems that semantically acyclic unions of CQs---i.e., unions of CQs that are equivalent to a union of acyclic ones---can be evaluated in polynomial time, though testing membership in the class of semantically acyclic CQs is NP-complete. We study here the fundamental notion of semantic acyclicity in the context of graph databases and unions of conjunctive regular path queries with inverse (UC2RPQs). It is known that unions of acyclic C2RPQs can be evaluated efficiently, but it is by no means obvious whether similarly good evaluation properties hold for the class of UC2RPQs that are semantically acyclic. We prove that checking whether a UC2RPQ is semantically acyclic is Expspace-complete and obtain as a corollary that evaluation of semantically acyclic UC2RPQs is fixed-p...

Skyline Path Queries With Aggregate Attributes

The skyline path query is a novel extension of skyline queries. A skyline path query retrieves a set of non-dominated paths from origin $s$ to destination $t$ . On a road network using multiple path criteria, such as the distance, travel time, and number of travelers on a path, this paper extends the concept of skyline path query by considering a new type of criteria referred to as the aggregate attribute of paths. The method used for calculating this type of criteria is very different from that of existing criteria, and this can have a notable effect on the processing of ordinary skyline path queries. This paper defines the aggregate attributes of paths, discusses the impact of aggregate attributes on skyline path queries, and proposes a novel index tree with an intelligent algorithm to find the skyline path while taking aggregate attributes into account. Experiments demonstrate the effectiveness and efficiency of the proposed algorithm.

Datalog Rewritings of Regular Path Queries using Views

We consider query answering using views on graph databases, i.e. databases structured as edge-labeled graphs. We mainly consider views and queries specified by Regular Path Queries (RPQ). These are queries selecting pairs of nodes in a graph database that are connected via a path whose sequence of edge labels belongs to some regular language. We say that a view V determines a query Q if for all graph databases D, the view image V(D) always contains enough information to answer Q on D. In other words, there is a well defined function from V(D) to Q(D). Our main result shows that when this function is monotone, there exists a rewriting of Q as a Datalog query over the view instance V(D). In particular the rewriting query can be evaluated in time polynomial in the size of V(D). Moreover this implies that it is decidable whether an RPQ query can be rewritten in Datalog using RPQ views.

Fault detection and recovery scheme for routing and lifetime enhancement in WSN

In WSNs, failures are unavoidable due to the inhospitable environment and unattended deployment. The node failure leads to disconnection from the network and causes network partitioning. We propose to develop a fault detection and recovery scheme where the sink generates an agent packet and the Agent forms a query path towards the dead or faulty node. Here, sink periodically broadcasts the Agent packet to all its neighbor nodes. The receiving node randomly makes a decision as whether to forward the packet or not thereby detecting the dead or faulty nodes. After detecting a node failure or dead node, the connectivity is restored using Least-Disruptive Topology Repair (LeDiR) without extending the length of the shortest path among nodes compared to the pre failure topology. LeDiR replaces the faulty node with block movement.

Expressive Path Queries on Graph with Data

Graph data models have recently become popular owing to their applications, e.g., in social networks and the semantic web. Typical navigational query languages over graph databases - such as Conjunctive Regular Path Queries (CRPQs) - cannot express relevant properties of the interaction between the underlying data and the topology. Two languages have been recently proposed to overcome this problem: walk logic (WL) and regular expressions with memory (REM). In this paper, we begin by investigating fundamental properties of WL and REM, i.e., complexity of evaluation problems and expressive power. We first show that the data complexity of WL is nonelementary, which rules out its practicality. On the other hand, while REM has low data complexity, we point out that many natural data/topology properties of graphs expressible in WL cannot be expressed in REM. To this end, we propose register logic, an extension of REM, which we show to be able to express many natural graph properties expressible in WL, while at the same time preserving the elementariness of data complexity of REMs. It is also incomparable to WL in terms of expressive power.

Video Copy Detection Based on Path Merging and Query Content Prediction

Content-based video copy detection is undoubtedly one of the most effective solutions to video content tracing and copyright protection. It extracts features from videos and determines whether a copy occurs by comparing the extracted features. While a lot of work has been reported to address this problem with good performance, very few considered it from the perspective of a dynamic searching process. In this paper, we treat the copy detection in video streams as a sequential matching problem and take into consideration the connections between temporary results and forthcoming input. Specifically, we propose a video copy detection system that involves a novel frame fusion scheme and an adaptive search strategy. The proposed frame fusion scheme relies on path merging in a graph model, which is able to work in an online manner and provide informative temporary fusion results. Based on these temporary results, query content predictions can be generated, which will be fed back to the frame search engine to instruct it to adaptively adjust the search strategy. The experimental results show that the proposed frame fusion scheme achieves competitive detection and localization accuracies compared with the state-of-the-art methods. Meanwhile, with the assistance of the adaptive search strategy, the computational complexity of frame similarity search is dramatically reduced at a cost of a slight decrease in accuracy.

Beyond Millisecond Latency <inline-formula><tex-math>$k$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="u-ieq1-2426702.gif"/></alternatives></inline-formula>NN Search on Commodity Machine

The $k$ nearest neighbor ( $k$ NN) search on road networks is an important function in web mapping services. These services are now dealing with rapidly arriving queries, that are issued by a massive amount of users. While overlay graph-based indices can answer shortest path queries efficiently, there have been no studies on utilizing such indices to answer $k$ NN queries efficiently. In this paper, we fill this research gap and present two efficient $k$ NN search solutions on overlay graph-based indices. Experimental results show that our solutions offer very low query latency (0.1 ms) and require only small index sizes, even for 10-million-node networks.

Alternative Trust Sources

DNSSEC has been in development for 20 years. It provides for provable security when retrieving domain names through the use of a public key infrastructure (PKI). Unfortunately, there is also significant overhead involved with DNSSEC: verifying certificate chains of signed DNS messages involves extra computation, queries to remote resolvers, additional transfers, and introduces added latency into the DNS query path. We pose the question: is it possible to achieve practical security without always verifying this certificate chain if we use a different, outside source of trust between resolvers? We believe we can. Namely, by using a long-lived, mutually authenticated TLS connection between pairs of DNS resolvers, we suggest that we can maintain near-equivalent levels of security with very little extra overhead compared to a non-DNSSEC enabled resolver. By using a reputation system or probabilistically verifying a portion of DNSSEC responses would allow for near-equivalent levels of security to be reached, even in the face of compromised resolvers.

On uncertain graphs modeling and queries

Large-scale, highly-interconnected networks pervade both our society and the natural world around us. Uncertainty, on the other hand, is inherent in the underlying data due to a variety of reasons, such as noisy measurements, lack of precise information needs, inference and prediction models, or explicit manipulation, e.g., for privacy purposes. Therefore, uncertain, or probabilistic, graphs are increasingly used to represent noisy linked data in many emerging application scenarios, and they have recently become a hot topic in the database research community. While many classical graph algorithms such as reachability and shortest path queries become # P -complete, and hence, more expensive in uncertain graphs; various complex queries are also emerging over uncertain networks, such as pattern matching, information diffusion, and influence maximization queries. In this tutorial, we discuss the sources of uncertain graphs and their applications, uncertainty modeling, as well as the complexities and algorithmic advances on uncertain graphs processing in the context of both classical and emerging graph queries. We emphasize the current challenges and highlight some future research directions.

G-Tree: An Efficient and Scalable Index for Spatial Search on Road Networks

In the recent decades, we have witnessed the rapidly growing popularity of location-based systems. Three types of location-based queries on road networks, single-pair shortest path query, $k$ nearest neighbor ( $k$ NN) query, and keyword-based $k$ NN query, are widely used in location-based systems. Inspired by $\tt R$ - $\tt tree$ , we propose a height-balanced and scalable index, namely $\tt G$ - $\tt tree$ , to efficiently support these queries. The space complexity of $\tt G$ - $\tt tree$ is $\mathcal {O}(|\mathcal {V}|\log {|\mathcal {V}|})$ where ${|\mathcal {V}|}$ is the number of vertices in the road network. Unlike previous works that support these queries separately, $\tt G$ - $\tt tree$ supports all these queries within one framework. The basis for this framework is an assembly-based method to calculate the shortest-path distances between two vertices. Based on the assembly-based method, efficient search algorithms to answer $k$ NN queries and keyword-based $k$ NN queries are developed. Experiment results show $\tt G$ - $\tt tree$ ’s theoretical and practical superiority over existing methods.

Regular Path Queries in Lightweight Description Logics: Complexity and Algorithms

Conjunctive regular path queries are an expressive extension of the well-known class of conjunctive queries. Such queries have been extensively studied in the (graph) database community, since they support a controlled form of recursion and enable sophisticated path navigation. Somewhat surprisingly, there has been little work aimed at using such queries in the context of description logic (DL) knowledge bases, particularly for the lightweight DLs that are considered best suited for data-intensive applications. This paper aims to bridge this gap by providing algorithms and tight complexity bounds for answering two-way conjunctive regular path queries over DL knowledge bases formulated in lightweight DLs of the DL-Lite and EL families. Our results demonstrate that in data complexity, the cost of moving to this richer query language is as low as one could wish for: the problem is NL-complete for DL-Lite and P-complete for EL. The combined complexity of query answering increases from NP- to PSpace-complete, but for two-way regular path queries (without conjunction), we show that query answering is tractable even with respect to combined complexity. Our results reveal two-way conjunctive regular path queries as a promising language for querying data enriched by ontologies formulated in DLs of the DL-Lite and EL families or the corresponding OWL 2 QL and EL profiles.

Approximate path searching for supporting shortest path queries on road networks

Although various efficient indexing techniques have been proposed to improve the performance in distributing road data to mobile clients to perform shortest path searches on road networks, how to minimize the tune-in and path searching costs are still important performance goals since the clients usually have limited processing capability and energy supply. In this paper, based on the Next Region (NR) approach, we propose a new method called Approximate Path Searching (APS) for constructing the broadcast index at mobile clients with soft arrival times to destinations. APS adopts an approximation technique in calculating the sets of required regions for each <start, destination> region pair in a road network to reduce the index generation cost at the road server and the tune-in and path searching costs at the clients. Different from NR, which only provides one set of required regions containing the shortest path from a client’s start region to its destination region, APS generates n-sets of required regions for a client to choose. Each set of required regions contains different numbers of regions. Although choosing a smaller region set may give a slightly longer delay in arrival, the tune-in and path searching costs could be significantly reduced. Theoretical analysis and extensive simulation experiments have been performed to demonstrate the effectiveness of APS in improving the system performance with just a few percentages delay in arrival time on average comparing with NR using data sets from real road networks.

Threshold-Based Shortest Path Query over Large Correlated Uncertain Graphs

With the popularity of uncertain data, queries over uncertain graphs have become a hot topic in the database community. As one of the important queries, the shortest path query over an uncertain graph has attracted much attention of researchers due to its wide applications. Although there are some efficient solutions addressing this problem, all existing models ignore an important property existing in uncertain graphs: the correlation among the edges sharing the same vertex. In this paper, we apply Markov network to model the hidden correlation in uncertain graphs and compute the shortest path. Unfortunately, calculating the shortest path and corresponding probability over uncertain graphs modeled by Markov networks is a #P-hard problem. Thus, we propose a filtering-and-verification framework to accelerate the queries. In the filtering phase, we design a probabilistic shortest path index based on vertex cuts and blocks of a graph. We find a series of upper bounds and prune the vertices and edges whose upper bounds of the shortest path probability are lower than the threshold. By carefully picking up the blocks and vertex cuts, the index is optimized to have the maximum pruning capability, so that we can filter a large number of vertices which make no contribution to the final shortest path query results. In the verification phase, we develop an efficient sampling algorithm to determine the final query answers. Finally, we verify the efficiency and effectiveness of our solutions with extensive experiments.

A Counting Logic for Trees

It has been recently shown that the fully enriched μ -calculus, an expressive modal logic, is undecidable. In the current work, we prove that this result does not longer hold when considering finite tree models. This is achieved with the introduction of an extension of the fully enriched μ -calculus for trees with numerical constraints. Contrastively with graded modalities, which restrict the occurrence of immediate successor nodes only, the logic introduced in this paper can concisely express numerical constraints on any tree region, as for instance the ancestor or descendant nodes. In order to show that the logic is in EXPTIME, we also provide a corresponding satisfiability algorithm. By succinct reductions to the logic, we identify several decidable extension of regular tree languages with counting and interleaving operators. It is also shown that XPath extensions with counting constructs on regular path queries can be concisely captured by the logic. Finally, we show that several XML reasoning problems (XPath queries with schemas), such as emptiness and containment, can be optimally solved with the satisfiability algorithm.

Efficient path query and reasoning method based on rare axis

A new concept of rare axis based on statistical facts is proposed, and an evaluation algorithm is designed thereafter. For the nested regular expressions containing rare axes, the proposed algorithm can reduce its evaluation complexity from polynomial time to nearly linear time. The distributed technique is also employed to construct the navigation axis indexes for resource description framework (RDF) graph data. Experiment results in DrugBank and BioGRID show that this method can improve the query efficiency significantly while ensuring the accuracy and meet the query requirements on Web-scale RDF graph data.