We study an important practical aspect of the route planning problem in real-world road networks -- maneuvers. Informally, maneuvers represent various irregularities of the road network graph such as turn-prohibitions, traffic light delays, round-abouts, forbidden passages and so on. We propose a generalized model which can handle arbitrarily complex (and even negative) maneuvers, and outline how to enhance Dijkstra's algorithm in order to solve route planning queries in this model without prior adjustments of the underlying road network graph.
Basically, the research directions are represented either by modifications of the underlying graph during preprocessing @cite_14 @cite_7 @cite_2 , or by adjusting a query algorithm @cite_0 @cite_4 in order to resolve simple types of restrictions during queries.
@cite_14 @cite_4 @cite_7 @cite_0 @cite_2
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In traffic research, management, and planning a number of path-based analyses are heavily used, e.g., for computing turn-times, evaluating green waves, or studying traffic flow. These analyses require retrieving the trajectories that follow the full path being analyzed. Existing path queries cannot sufficiently support such path-based analyses because they retrieve all trajectories that touch any edge in the path. In this paper, we define and formalize the strict path query. This is a novel query type tailored to support path-based analysis, where trajectories must follow all edges in the path. To efficiently support strict path queries, we present a novel NET work-constrained TRAjectory index (NETTRA). This index enables very efficient retrieval of trajectories that follow a specific path, i.e., strict path queries. NETTRA uses a new path encoding scheme that can determine if a trajectory follows a specific path by only retrieving data from the first and last edge in the path. To correctly answer strict path queries existing network-constrained trajectory indexes must retrieve data from all edges in the path. An extensive performance study of NETTRA using a very large real-world trajectory data set, consisting of 1.7 million trajectories (941 million GPS records) and a road network with 1.3 million edges, shows a speed-up of two orders of magnitude compared to state-of-the-art trajectory indexes. We consider the problem of using a large unlabeled sample to boost performance of a learning algorit,hrn when only a small set of labeled examples is available. In particular, we consider a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views. For example, the description of a web page can be partitioned into the words occurring on that page, and the words occurring in hyperlinks t,hat point to that page. We assume that either view of the example would be sufficient for learning if we had enough labeled data, but our goal is to use both views together to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples. Specifically, the presence of two distinct views of each example suggests strategies in which two learning algorithms are trained separately on each view, and then each algorithm’s predictions on new unlabeled examples are used to enlarge the training set of the other. Our goal in this paper is to provide a PAC-style analysis for this setting, and, more broadly, a PAC-style framework for the general problem of learning from both labeled and unlabeled data. We also provide empirical results on real web-page data indicating that this use of unlabeled examples can lead to significant improvement of hypotheses in practice. *This research was supported in part by the DARPA HPKB program under contract F30602-97-1-0215 and by NSF National Young investigator grant CCR-9357793. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. TO copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and or a fee. COLT 98 Madison WI USA Copyright ACM 1998 l-58113-057--0 98 7... 5.00 92 Tom Mitchell School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3891 mitchell+@cs.cmu.edu We address the problem of graph rewriting and narrowing as the underlying operational semantics of rule-based programming languages. We propose new optimal graph rewriting and narrowing strategies in the setting of orthogonal constructor-based graph rewriting systems. For this purpose, we first characterize a subset of graphs, called admissible graphs. A graph is admissible if none of its defined operations belongs to a cycle. We then prove the confluence, as well as the confluence modulo bisimilarity (unraveling), of the admissible graph rewriting relation. Afterwards, we define a sequential graph rewriting strategy by using Antoy’s definitional trees. We show that the resulting strategy computes only needed redexes and develops optimal derivations w.r.t. the number of steps. Finally, we tackle the graph narrowing relation over admissible graphs and propose a sequential narrowing strategy which computes independent solutions and develops shorter derivations than most general graph narrowing. The sheer volume of scholarly publications available online significantly challenges how scholars retrieve the new information available and locate the candidate reference papers. While classical text retrieval and pseudo relevance feedback (PRF) algorithms can assist scholars in accessing needed publications, in this study, we propose an innovative publication ranking method with PRF by leveraging a number of meta-paths on the heterogeneous bibliographic graph. Different meta-paths on the graph address different ranking hypotheses, whereas the pseudo-relevant papers (from the retrieval results) are used as the seed nodes on the graph. Meanwhile, unlike prior studies, we propose "restricted meta-path" facilitated by a new context-rich heterogeneous network extracted from full-text publication content along with citation context. By using learning-to-rank, we integrate 18 different meta-path-based ranking features to derive the final ranking scores for candidate cited papers. Experimental results with ACM full-text corpus show that meta-path-based ranking with PRF on the new graph significantly (p While the volume of scholarly publications has increased at a frenetic pace, accessing and consuming the useful candidate papers, in very large digital libraries, is becoming an essential and challenging task for scholars. Unfortunately, because of language barrier, some scientists (especially the junior ones or graduate students who do not master other languages) cannot efficiently locate the publications hosted in a foreign language repository. In this study, we propose a novel solution, cross-language citation recommendation via Hierarchical Representation Learning on Heterogeneous Graph (HRLHG), to address this new problem. HRLHG can learn a representation function by mapping the publications, from multilingual repositories, to a low-dimensional joint embedding space from various kinds of vertexes and relations on a heterogeneous graph. By leveraging both global (task specific) plus local (task independent) information as well as a novel supervised hierarchical random walk algorithm, the proposed method can optimize the publication representations by maximizing the likelihood of locating the important cross-language neighborhoods on the graph. Experiment results show that the proposed method can not only outperform state-of-the-art baseline models, but also improve the interpretability of the representation model for cross-language citation recommendation task. This paper surveys and compares various strategies for processing logic queries in relational databases. The survey and comparison is limited to the case of Horn Clauses with evaluable predicates but without function symbols. The paper is organized in three parts. In the first part, we introduce the main concepts and definitions. In the second, we describe the various strategies. For each strategy, we give its main characteristics, its application range and a detailed description. We also give an example of a query evaluation. The third part of the paper compares the strategies on performance grounds. We first present a set of sample rules and queries which are used for the performance comparisons, and then we characterize the data. Finally, we give an analytical solution for each query rule system. Cost curves are plotted for specific configurations of the data. In this paper, we study the problem of subgraph matching that extracts all subgraph isomorphic embeddings of a query graph q in a large data graph G. The existing algorithms for subgraph matching follow Ullmann's backtracking approach; that is, iteratively map query vertices to data vertices by following a matching order of query vertices. It has been shown that the matching order of query vertices is a very important aspect to the efficiency of a subgraph matching algorithm. Recently, many advanced techniques, such as enforcing connectivity and merging similar vertices in query or data graphs, have been proposed to provide an effective matching order with the aim to reduce unpromising intermediate results especially the ones caused by redundant Cartesian products. In this paper, for the first time we address the issue of unpromising results by Cartesian products from "dissimilar" vertices. We propose a new framework by postponing the Cartesian products based on the structure of a query to minimize the redundant Cartesian products. Our second contribution is proposing a new path-based auxiliary data structure, with the size O(|E(G)| x |V(q)|), to generate a matching order and conduct subgraph matching, which significantly reduces the exponential size O(|V(G)||V(q)|-1) of the existing path-based auxiliary data structure, where V (G) and E (G) are the vertex and edge sets of a data graph G, respectively, and V (q) is the vertex set of a query @math . Extensive empirical studies on real and synthetic graphs demonstrate that our techniques outperform the state-of-the-art algorithms by up to @math orders of magnitude. Research data and publications are usually stored in separate and structurally distinct information systems. Often, links between these resources are not explicitly available which complicates the search for previous research. In this paper, we propose a pattern induction method for the detection of study references in full texts. Since these references are not specified in a standardized way and may occur inside a variety of different contexts --- i.e., captions, footnotes, or continuous text --- our algorithm is required to induce very flexible patterns. To overcome the sparse distribution of training instances, we induce patterns iteratively using a bootstrapping approach. We show that our method achieves promising results for the automatic identification of data references and is a first step towards building an integrated information system. We consider a parsed text corpus as an instance of a labelled directed graph, where nodes represent words and weighted directed edges represent the syntactic relations between them. We show that graph walks, combined with existing techniques of supervised learning, can be used to derive a task-specific word similarity measure in this graph. We also propose a new path-constrained graph walk method, in which the graph walk process is guided by high-level knowledge about meaningful edge sequences (paths). Empirical evaluation on the task of named entity coordinate term extraction shows that this framework is preferable to vector-based models for small-sized corpora. It is also shown that the path-constrained graph walk algorithm yields both performance and scalability gains. In this paper, we present the results of a user study on exploratory search activities in a social science digital library. We conducted a user study with 32 participants with a social sciences background—16 postdoctoral researchers and 16 students—who were asked to solve a task on searching related work to a given topic. The exploratory search task was performed in a 10-min time slot. The use of certain search activities is measured and compared to gaze data recorded with an eye tracking device. We use a novel tree graph representation to visualise the users’ search patterns and introduce a way to combine multiple search session trees. The tree graph representation is capable of creating one single tree for multiple users and identifying common search patterns. In addition, the information behaviour of students and postdoctoral researchers is being compared. The results show that search activities on the stratagem level are frequently utilised by both user groups. The most heavily used search activities were keyword search, followed by browsing through references and citations, and author searching. The eye tracking results showed an intense examination of documents metadata, especially on the level of citations and references. When comparing the group of students and postdoctoral researchers, we found significant differences regarding gaze data on the area of the journal name of the seed document. In general, we found a tendency of the postdoctoral researchers to examine the metadata records more intensively with regard to dwell time and the number of fixations. By creating combined session trees and deriving subtrees from those, we were able to identify common patterns like economic (explorative) and exhaustive (navigational) behaviour. Our results show that participants utilised multiple search strategies starting from the seed document, which means that they examined different paths to find related publications. The problem of processing shortest pa th queries in graphs arises in application areas such as intelligent t ranspor ta t ion system (ITS), geographic information system (GIS), and robotics. In this paper, we present an efficient algorithmic solution for processing shortest pa th queries in undirected planar graphs with non-negative edge weights. Previous algorithms for this problem on planar graphs all have a tradeoff between the query t ime and the da ta structure space used by the solutions. The previously best known trade-off on an n-vertex planar graph G in the worst case is O(v -r) t ime for a pa th length query and O(v -r + L) t ime for report ing an actual shortest path, with a da ta structure of O(n2 x ) space, where r is an integer parameter with 1 < r _< n and L is the number of edges on the output shortest path. We present a new scheme, called frame search, that exploits the graph planari ty in a novel fashion. Wi th this scheme, we build an improved da ta structure of O(n + pv -r + p2 r) space (in O(n + p2 v + pr 3 4) time), where p is the minimum cardinality of a face-covering of the planar embedding of G with 1 < p < O(n), and r is an integer parameter with 1 < r < p. This da ta structure enables us to process each length query in O(v -rlog r + a(n)) t ime and report an actual shortest path in an addit ional O(L) time, where a(n) is the inverse of Ackermann's function. In the worst case, our approach reduces the previously best query t ime by a factor of up to O(n 1 4) if the same amount of space is used. Our technique can also be applied to improve the previous query algorithms for some geometric shortest pa th problems on the plane. We consider the point-to-point (approximate) shortest-path query problem, which is the following generalization of the classical single-source (SSSP) and all-pairs shortest-path (APSP) problems: we are first presented with a network (graph). A so-called preprocessing algorithm may compute certain information (a data structure or index) to prepare for the next phase. After this preprocessing step, applications may ask shortest-path or distance queries, which should be answered as fast as possible. Due to its many applications in areas such as transportation, networking, and social science, this problem has been considered by researchers from various communities (sometimes under different names): algorithm engineers construct fast route planning methods; database and information systems researchers investigate materialization tradeoffs, query processing on spatial networks, and reachability queries; and theoretical computer scientists analyze distance oracles and sparse spanners. Related problems are considered for compact routing and distance labeling schemes in networking and distributed computing and for metric embeddings in geometry as well. In this survey, we review selected approaches, algorithms, and results on shortest-path queries from these fields, with the main focus lying on the tradeoff between the index size and the query time. We survey methods for general graphs as well as specialized methods for restricted graph classes, in particular for those classes with arguable practical significance such as planar graphs and complex networks. Shortest paths and shortest path distances are important primary queries for users to query in a large graph. In this paper, we propose a new approach to answer shortest path and shortest path distance queries efficiently with an error bound. The error bound is controlled by a user-specified parameter, and the online query efficiency is achieved with prepossessing offline. In the offline preprocessing, we take a reference node embedding approach which computes the single-source shortest paths from each reference node to all the other nodes. To guarantee the user-specified error bound, we design a novel coverage-based reference node selection strategy, and show that selecting the optimal set of reference nodes is NP-hard. We propose a greedy selection algorithm which exploits the submodular property of the formulated objective function, and use a graph partitioning-based heuristic to further reduce the offline computational complexity of reference node embedding. In the online query answering, we use the precomputed distances to provide a lower bound and an upper bound of the true shortest path distance based on the triangle inequality. In addition, we propose a linear algorithm which computes the approximate shortest path between two nodes within the error bound. We perform extensive experimental evaluation on a large-scale road network and a social network and demonstrate the effectiveness and efficiency of our proposed methods. The growing popularity of graph databases has generated interesting data management problems, such as subgraph search, shortest-path query, reachability verification, and pattern match. Among these, a pattern match query is more flexible compared to a subgraph search and more informative compared to a shortest-path or reachability query. In this paper, we address pattern match problems over a large data graph G. Specifically, given a pattern graph (i.e., query Q), we want to find all matches (in G) that have the similar connections as those in Q. In order to reduce the search space significantly, we first transform the vertices into points in a vector space via graph embedding techniques, coverting a pattern match query into a distance-based multi-way join problem over the converted vector space. We also propose several pruning strategies and a join order selection method to process join processing efficiently. Extensive experiments on both real and synthetic datasets show that our method outperforms existing ones by orders of magnitude. In this paper we settle several longstanding open problems in theory of indexability and external orthogonal range searching. In the rst part of the paper, we apply the theory of indexability to the problem of two-dimensional range searching. We show that the special case of 3-sided querying can be solved with constant redundancy and access overhead. From this, we derive indexing schemes for general 4-sided range queries that exhibit an optimal tradeo between redundancy and access overhead. In the second part of the paper, we develop dynamic external memory data structures for the two query types. Our structure for 3-sided queries occupies O(N=B) disk blocks, and it supports insertions and deletions in O(log B N) I Os and queries in O(log B N + T=B) I Os, where B is the disk block size, N is the number of points, and T is the query output size. These bounds are optimal. Our structure for general (4-sided) range searching occupies O (N=B)(log(N=B))= log log B N disk blocks and answers queries in O(log B N + T=B) I Os, which are optimal. It also supports updates in O (log B N)(log(N=B))= log log B N I Os. Center for Geometric Computing, Department of Computer Science, Duke University, Box 90129, Durham, NC 27708 0129. Supported in part by the U.S. Army Research O ce through MURI grant DAAH04 96 1 0013 and by the National Science Foundation through ESS grant EIA 9870734. Part of this work was done while visiting BRICS, Department of Computer Science, University of Aarhus, Denmark. Email: large@cs.duke.edu. yDepartment of Computer Sciences, University of Texas at Austin, Austin, TX 78712-1188. Email vsam@cs.utexas.edu zCenter for Geometric Computing, Department of Computer Science, Duke University, Box 90129, Durham, NC 27708 0129. Supported in part by the U.S. Army Research O ce through MURI grant DAAH04 96 1 0013 and by the National Science Foundation through grants CCR 9522047 and EIA 9870734. Part of this work was done while visiting BRICS, Department of Computer Science, University of Aarhus, Denmark and I.N.R.I.A., Sophia Antipolis, France. Email: jsv@cs.duke.edu. In multimedia information retrieval, most classic approaches tend to represent different modalities of media in the same feature space. Existing approaches take either one-to-one paired data or uni-directional ranking examples (i.e., utilizing only text-query-image ranking examples or image-query-text ranking examples) as training examples, which do not make full use of bi-directional ranking examples (bi-directional ranking means that both text-query-image and image-query-text ranking examples are utilized in the training period) to achieve a better performance. In this paper, we consider learning a cross-media representation model from the perspective of optimizing a listwise ranking problem while taking advantage of bi-directional ranking examples. We propose a general cross-media ranking algorithm to optimize the bi-directional listwise ranking loss with a latent space embedding, which we call Bi-directional Cross-Media Semantic Representation Model (Bi-CMSRM). The latent space embedding is discriminatively learned by the structural large margin learning for optimization with certain ranking criteria (mean average precision in this paper) directly. We evaluate Bi-CMSRM on the Wikipedia and NUS-WIDE datasets and show that the utilization of the bi-directional ranking examples achieves a much better performance than only using the uni-directional ranking examples. Traditional fact checking by experts and analysts cannot keep pace with the volume of newly created information. It is important and necessary, therefore, to enhance our ability to computationally determine whether some statement of fact is true or false. We view this problem as a link-prediction task in a knowledge graph, and present a discriminative path-based method for fact checking in knowledge graphs that incorporates connectivity, type information, and predicate interactions. Given a statement S of the form (subject, predicate, object), for example, (Chicago, capitalOf, Illinois), our approach mines discriminative paths that alternatively define the generalized statement (U.S. city, predicate, U.S. state) and uses the mined rules to evaluate the veracity of statement S . We evaluate our approach by examining thousands of claims related to history, geography, biology, and politics using a public, million node knowledge graph extracted from Wikipedia and PubMedDB. Not only does our approach significantly outperform related models, we also find that the discriminative predicate path model is easily interpretable and provides sensible reasons for the final determination. This article deals with the computation of consistent answers to queries on relational databases that violate primary key constraints. A repair of such inconsistent database is obtained by selecting a maximal number of tuples from each relation without ever selecting two distinct tuples that agree on the primary key. We are interested in the following problem: Given a Boolean conjunctive query q, compute a Boolean first-order (FO) query @j such that for every database db, @j evaluates to true on db if and only if q evaluates to true on every repair of db. Such @j is called a consistent FO rewriting of q. We use novel techniques to characterize classes of queries that have a consistent FO rewriting. In this way, we are able to extend previously known classes and discover new ones. Finally, we use an Ehrenfeucht-Fraisse game to show the non-existence of a consistent FO rewriting for @[email protected]?y(R([email protected]?,y)@?R([email protected]?,c)), where c is a constant and the first coordinate of R is the primary key. This paper proposes a novel approach named AGM to efficiently mine the association rules among the frequently appearing substructures in a given graph data set. A graph transaction is represented by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through the extended algorithm of the basket analysis. Its performance has been evaluated for the artificial simulation data and the carcinogenesis data of Oxford University and NTP. Its high efficiency has been confirmed for the size of a real-world problem. Constrained path problems have to do with finding paths in graphs subject to constraints. We present a constraint programming approach for solving the Ordered disjoint-paths problem (ODP), i.e., the Disjoint-paths problem where the pairs are associated with ordering constraints. In our approach, we reduce ODP to the Ordered simple path with mandatory nodes problem (OSPMN), i.e., the problem of finding a simple path containing a set of mandatory nodes in a given order. The reduction of the problem is motivated by the fact that we have an appropriate way of dealing with OSPMN based on DomReachability, a propagator that implements a generalized reachability constraint on a directed graph based on the concept of graph variables. The DomReachability constraint has three arguments: (1) a flow graph, i.e., a directed graph with a source node; (2) the dominance relation graph on nodes and edges of the flow graph; and (3) the transitive closure of the flow graph. Our experimental evaluation of DomReachability shows that it provides strong pruning, obtaining solutions with very little search. Furthermore, we show that DomReachability is also useful for defining a good labeling strategy. These experimental results give evidence that DomReachability is a useful primitive for solving constrained path problems over directed graphs.