No abstract

When integrating geo-spatial data sets, a join algorithm is used for finding sets of corresponding objects (i.e., objects that represent the same real-world entity). This article investigates location-based join algorithms for integration of several data sets. First, algorithms for integration of two data sets are presented and their performances, in terms of recall and precision, are compared. Then, two approaches for integration of more than two data sets are described. In one approach, all the integrated data sets are processed simultaneously. In the second approach, a join algorithm for two data sets is applied sequentially, either in a serial manner, where in each join at least one of the joined data sets is a single source, or in a hierarchical manner, where two join results can be joined. For the two approaches, join algorithms are given. The algorithms are designed to perform well even when location of objects are imprecise and each data set represents only some of the real-world entities. Results of extensive experiments with the different approaches are provided and analyzed. The experiments show the differences, in accuracy and efficiency, between the approaches, under different circumstances. The results also show that all our algorithms have much better accuracy than applying the commonly used one-sided nearest-neighbor join.

In a geographical route search, given search terms, the goal is to find an effective route that (1) starts at a given location, (2) ends at a given location, and (3) travels via geographical entities that are relevant to the given terms. A route is effective if it does not exceed a given distance limit whereas the ranking scores of the visited entities, with respect to the search terms, are maximal. This paper introduces route-search queries, suggests three semantics for such queries and deals with the problem of efficiently answering queries under the different semantics. Since the problem of answering route-search queries is a generalization of the traveling salesman problem, it is unlikely to have an efficient solution, i.e., there is no polynomial-time algorithm that solves the problem (unless P=NP). Hence, in this work we consider heuristics for the problem. Methods for effectively computing routes are presented. The methods are compared analytically and experimentally. For these methods, experiments on both synthetic and real-world data illustrate their efficiency and their effectiveness in computing a route that satisfies the constraints of a route-search query.

Recording the location of people using location-acquisition technologies, such as GPS, allows generating life patterns, which associate people to places they frequently visit. Considering life patterns as edges that connect users of a social network to geographical entities on a spatial network, enriches the social network, providing an integrated sociospatial graph. Queries over such graph extract information on users, in correspondence with their location history, and extract information on geographical entities in correspondence with users who frequently visit these entities.In this paper we present the concept of a socio-spatial graph that is based on life patterns, where users are connected to geographical entities using life-pattern edges. We provide a set of operators that form a query language suitable for the integrated data. We consider two implementations of a socio-spatial graph storage-one implementation uses a relational database system as the underline data storage, and the other employs a graph database system. The two implementations are compared, experimentally, for various queries and data. An important contribution of this work is in illustrating the usefulness and the feasibility of maintaining and querying integrated socio-spatial graphs.

Cellular phones and GPS-based navigation systems allow recording the location history of users, to find places the users frequently visit and routes along which the users frequently travel. This provides associations between users and geographic entities. Considering these associations as edges that connect users of a social network to geographical entities on a spatial network yields an integrated socio-spatial network. Queries over a socio-spatial network glean information on users, in correspondence with their location history, and retrieve geographical entities in association with the users who frequently visit these entities.In this paper we present a graph model for socio-spatial networks that store information on frequently traveled routes. We present a query language that consists of graph traversal operations, aiming at facilitating the formulation of queries, and we show how queries over the network can be evaluated efficiently. We also show how social-based route recommendation can be implemented using our query language. We describe an implementation of the suggested model over a graph-based database system and provide an experimental evaluation, to illustrate the effectiveness of our model.