Efficient Spatial Keyword Search in Trajectory Databases

An increasing amount of trajectory data is being annotated with text descriptions to better capture the semantics associated with locations. The fusion of spatial locations and text descriptions in trajectories engenders a new type of top-$k$ queries that take into account both aspects. Each trajectory in consideration consists of a sequence of geo-spatial locations associated with text descriptions. Given a user location $\lambda$ and a keyword set $\psi$, a top-$k$ query returns $k$ trajectories whose text descriptions cover the keywords $\psi$ and that have the shortest match distance. To the best of our knowledge, previous research on querying trajectory databases has focused on trajectory data without any text description, and no existing work has studied such kind of top-$k$ queries on trajectories. This paper proposes one novel method for efficiently computing top-$k$ trajectories. The method is developed based on a new hybrid index, cell-keyword conscious B$^+$-tree, denoted by \cellbtree, which enables us to exploit both text relevance and location proximity to facilitate efficient and effective query processing. The results of our extensive empirical studies with an implementation of the proposed algorithms on BerkeleyDB demonstrate that our proposed methods are capable of achieving excellent performance and good scalability.Comment: 12 page

Diversified spatial keyword search on road networks

With the increasing pervasiveness of the geo-positioning technologies, there is an enormous amount of spatio-textual objects available in many applications such as location based services and social networks. Consequently, various types of spatial keyword searches which explore both locations and textual descriptions of the objects have been intensively studied by the research communities and commercial organizations. In many important applications (e.g., location based services), the closeness of two spatial objects is measured by the road network distance. Moreover, the result diversification is becoming a common practice to enhance the quality of the search results. Motived by the above facts, in this paper we study the problem of diversified spatial keyword search on road networks which considers both the relevance and the spatial diversity of the results. An efficient signature-based inverted indexing technique is proposed to facilitate the spatial keyword query processing on road networks. Then we develop an efficient diversified spatial keyword search algorithm by taking advantage of spatial keyword pruning and diversity pruning techniques. Comprehensive experiments on real and synthetic data clearly demonstrate the efficiency of our methods

Efficient top K temporal spatial keyword search

Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale in many emerging applications such as location based services and social networks. Due to their importance, a large body of work has focused on efficiently computing various spatial keyword queries. In this paper, we study the top-k temporal spatial keyword query which considers three important constraints during the search including time, spatial proximity and textual relevance. A novel index structure, namely SSG-tree, to efficiently insert/delete spatio-temporal web objects with high rates. Base on SSG-tree an efficient algorithm is developed to support top-k temporal spatial keyword query. We show via extensive experimentation with real spatial databases that our method has increased performance over alternate techniques

Ranked Spatial-keyword Search over Web-accessible Geotagged Data: State of the Art

Search engines, such as Google and Yahoo!, provide efficient retrieval and ranking of web pages based on queries consisting of a set of given keywords. Recent studies show that 20% of all Web queries also have location constraints, i.e., also refer to the location of a geotagged web page. An increasing number of applications support location based keyword search, including Google Maps, Bing Maps, Yahoo! Local, and Yelp. Such applications depict points of interest on the map and combine their location with the keywords provided by the associated document(s). The posed queries consist of two conditions: a set of keywords and a spatial location. The goal is to find points of interest with these keywords close to the location. We refer to such a query as spatial-keyword query. Moreover, mobile devices nowadays are enhanced with built-in GPS receivers, which permits applications (such as search engines or yellow page services) to acquire the location of the user implicitly, and provide location-based services. For instance, Google Mobile App provides a simple search service for smartphones where the location of the user is automatically captured and employed to retrieve results relevant to her current location. As an example, a search for ”pizza” results in a list of pizza restaurants nearby the user. Given the popularity of spatial-keyword queries and their wide applicability in practical scenarios, it is critical to (i) establish mechanisms for efficient processing of spatial-keyword queries, and (ii) support more expressive query formulation by means of novel 1 query types. Although studies on both keyword search and spatial queries do exist, the problem of combining the search capabilities of both simultaneously has received little attention

Best Keyword Query Search Using Minimum Spatial Cover

Enlivened by the expanding fame of Mobile processing, administrations in view of area and with the accessibility of computerized maps, the spatial catchphrase look has achieved wide consideration. In spatial databases the relationship of items is finished with watchwords. The reason for existing is to locate various free protests, in which each item is closer to the area of question and the catchphrases related will be identified with the gathering of inquiry watchwords. The related watchword closeness is connected to gauge the connection among two gathered catchphrases. The idea of watchword spread, covers all related inquiry catchphrases which are nearer to each other. This methodology is known as m Closest Keywords (mCK) inquiry. The goal is to investigate a general structure, known as Best Keyword Cover (BKC) questions, which alongside bury objects separate additionally considers appraisals of catchphrase, which improves the basic leadership process. In BKC inquiry handling, two calculations are utilized: Baseline and Keyword Nearest Neighbor Expansion (KNNE). The gauge calculation is gotten from mCK question handling. The working of the gauge calculation diminishes radically as a result of incomprehensible catchphrase covers produced. To beat this disadvantage, a more extensible calculation KNNE is utilized. This calculation decreases the quantity of watchword spreads delivered

Adaptive Processing of Spatial-Keyword Data Over a Distributed Streaming Cluster

The widespread use of GPS-enabled smartphones along with the popularity of micro-blogging and social networking applications, e.g., Twitter and Facebook, has resulted in the generation of huge streams of geo-tagged textual data. Many applications require real-time processing of these streams. For example, location-based e-coupon and ad-targeting systems enable advertisers to register millions of ads to millions of users. The number of users is typically very high and they are continuously moving, and the ads change frequently as well. Hence sending the right ad to the matching users is very challenging. Existing streaming systems are either centralized or are not spatial-keyword aware, and cannot efficiently support the processing of rapidly arriving spatial-keyword data streams. This paper presents Tornado, a distributed spatial-keyword stream processing system. Tornado features routing units to fairly distribute the workload, and furthermore, co-locate the data objects and the corresponding queries at the same processing units. The routing units use the Augmented-Grid, a novel structure that is equipped with an efficient search algorithm for distributing the data objects and queries. Tornado uses evaluators to process the data objects against the queries. The routing units minimize the redundant communication by not sending data updates for processing when these updates do not match any query. By applying dynamically evaluated cost formulae that continuously represent the processing overhead at each evaluator, Tornado is adaptive to changes in the workload. Extensive experimental evaluation using spatio-textual range queries over real Twitter data indicates that Tornado outperforms the non-spatio-textually aware approaches by up to two orders of magnitude in terms of the overall system throughput

Report on novel spatial-keyword search techniques

Nowadays an increasing amount of web-accessible information on spatial objects becomes available to the public every day. Apart from the spatial location of an object (e.g., a point of interest), additional descriptive information typically includes textual description as well as various ratings, often user generated. Modern applications employ spatio-textual queries, which take into account both the spatial location of an object and its textual similarity to retrieve the most relevant objects. However, existing applications provide a limited functionality to the users. For example, several meaningful queries cannot be expressed by existing approaches and motivate our novel prototype system. In the first chapter, we address this limitation by supporting ranked retrieval of objects of interest by taking into account the quality of facilities in their vicinity, but also their textual similarity to user defined keywords. In the second chapter, we analyze the properties of geotagged photos of Flickr, and propose novel location-aware tag recommendation methods. Both of the above techniques are novel spatial-keyword search methods