International Journal of Computer Engineering and Applications, Volume XII, Issue II, Feb. 18, ISSN

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1 International Journal of Computer Engineering and Applications, Volume XII, Issue II, Feb. 18, ISSN SPATIAL KEYWORD QUERY PROCESSING: R*-IF TREE IMPLEMENTATION Sulbha K. Powar 1, Dr. Ganesh M. Magar 2 1 Research Scholar, P. G. Department of Computer Science, SNDT Women s University, Mumbai, India 2 Associate Professor, P. G. Department of Computer Science, SNDT Women s University,Mumbai, India ABSTRACT: The integration of a Geographic Information System (GIS) and internet technology has boosted the use of spatial keyword queries. Various types of hybrid geo-textual indexing techniques are developed that enable the indexing of data that contains both text descriptions and geo-locations to support the efficient processing of spatial keyword queries that take a geo-location and a set of keywords as arguments and return relevant contents that matches the arguments. A single index structure does not suit the needs of all types of queries. Hence it is required to measure the performance using different formulations of index structures and evaluating them in different scenarios. This paper presents implementation of R*-IF tree in four different ways. Keywords: Geo-Textual Indexing, Hybrid Indexing, Inverted file, R*-Tree, R*-IF Tree [1] INTRODUCTION Many applications involve large collections of geo-spatial objects with their latitude, longitude, and the attribute data. Queries that arise over geo-spatial data are of three main types: spatial range queries, nearest neighbor queries and spatial join queries [6]. The efficient management and analysis of such data is of great interest in a wide range of application domains. Few examples amongst wide range of applications of location-based services are weather forecasting, disaster management systems, telecom and network services, urban planning, transportation planning, agriculture, asset management, tourism, water management and wildlife Sulbha K. Powar and Ganesh M. Magar 65

2 SPATIAL KEYWORD QUERY PROCESSING: R*-IF TREE IMPLEMENTATION management [9]. Efficient algorithms for answering location-based queries, which will consider both locations as well as attribute data are needed.the techniques are required that enable the indexing of data that contains both text descriptions and geo-locations to support the efficient processing of spatial keyword queries that take a geo-location and a set of keywords as arguments and return relevant content that matches the arguments [10]. [2] GEO-TEXTUAL INDEXING There is significant commercial and research interest in location-based search. Searching of keywords belonging to one or more locations (geographic references) requires geographical search and ranking based on spatial and textual relevancy. For Example - Find the 5 nearest hospitalsfrom the query location having blood banks. Find all fire stations within 10 kilometres from a query location. This type of search sets the requirement of spatial and textual indexing. Search needs to balance both spatial proximity and textual relevance. Many spatial Index structures have been proposed. Some designed primarily to index collections of points, some handle region data, and some handle both. Examples of index structure for point data are Grid files, hb trees, KD trees, Point Quad trees, SR trees. Examples of index structure for region as well as point data are Region Quad trees. R trees, SKD trees. and many more. Many variants of these index structuresalso exist. There no consensus on the best spatial index structure. R trees are widely implemented in commercial DBMSs, because of their relative simplicity, ability to handle both point and region data and their performance. R trees recursively subdivides the multidimensional space by adapting the B+ tree idea to spatial data. Many variants of R-Trees exist, viz. Cell trees, Hilbert R trees, Packed R trees, R*trees, R+ trees, TV trees and X trees. Geo-textual Indexing can be classified using 3 different aspects, the spatial indexing scheme used, the text index employed, and hybrid manner of the spatial index and the text index. Spatial Indexing Schemehave mainly three categories namely R-tree based indices, grid-based indices, and space filling curve-based indices. Text Indexing Scheme are classified as Inverted file, Bitmap and Combination Scheme. The geo-textual indices combine spatial and text indexing in one of the tree ways text-first loose combination, spatial-first loose combination (Figure 1), and tight combination (Figure 2) [6]. Figure 1: R*-IF Tree - Spatial-First Loose Combination Sulbha K. Powar and Ganesh M. Magar 66

3 International Journal of Computer Engineering and Applications, Volume XII, Issue II, Feb. 18, ISSN Figure 2: R*-IF Tree - Spatial-First Tight Combination A R*-IF is a spatial-firstindex-inverted file. An R*-tree is first built for indexing all objects in Database without considering their text components. Next, for each leaf node of the R*-tree, an inverted file is created for indexing the text components of the objects contained in the leaf node. The IF-R*, a text-first geo-textual index, is the counterpart ofr*-if. For each distinct term t in D, a separate R*-tree is built forthe objects in D containing term t.r*-if and IF R*, both are proposed for tackling the problem of retrieving web documents relevant to a keyword query within a prespecified spatial region, i.e., theboolean Range Query(BRQ). At query time, for the R*-IF, a set of leaf nodes that intersect with the query region is retrieved first. Then, objects whose documents contain query keywords are returned as the result. For the IF-R*, all the R*-trees corresponding to the query keywords need to be accessed [6]. [3] RELATED WORK Various techniques are developed for hybrid indexing which use inverted file and R-tree. Different combining schemes of hybrid indexing structure, which integrate inverted files and R*- tree were proposed [12]. Inverted file and R*-tree double index, first inverted file then R*-and first R*-tree then inverted file scheme are evaluated It was found that, these have almost same storage cost but second is better than first and third in query time. Indexes based on R*-trees are confirmed to be more efficient than indexes based on grid structures. KR*-tree were implemented for processing spatial-keyword queries with the AND semantics by capturing the joint distribution of spatial keywords [7]. In this implementation, the pruning power of both space and text is exploited simultaneously. The performance has greatly enhanced by merging the two steps into one. Information retrieval R-tree (IR2-Tree) combines an R-tree with superimposed text signatures [4]. The tight integration of data structures and algorithms with each node containing both spatial and keyword information efficiently answers top-k spatial keyword queries. The IR-tree is studied and used extensively in variations of spatial keyword queries [13]. The framework proposed uses the inverted file for text retrieval and the R-tree for spatial proximity querying [3]. Several indexing approaches like IR-tree and its variants, document similarity IR tree (DIR-tree), the cluster enhanced IR-tree (CIR-tree) and the cluster enhanced DIR-tree (CDIR-tree) are explored within the framework for computing the top-k query. Hybrid spatial-keyword index (SKI)efficiently processes top-k spatial Boolean (k-sb) queries [1]. It is done by combining an R- Sulbha K. Powar and Ganesh M. Magar 67

4 SPATIAL KEYWORD QUERY PROCESSING: R*-IF TREE IMPLEMENTATION tree with an inverted index by the inclusion of spatial references in posting lists. Spatial inverted index (S2I) improves the performance of top-k spatial keyword queries [5]. Index maps each distinct term in the vocabulary into a distinct ar-tree or block that stores all objects with the given term. The objects are stored differently according to the document frequency of the term and can be retrieved efficiently in decreasing order of keyword relevance and spatial proximity. The spatial inverted list (SI-index) is a compressed version of an I-index with embedded coordinates [11]. In location based approximate-keyword (LBAK) queriesapproximate string search is used to identify for each query keyword those strings that are similar [8]. This is useful when users do not enter the correct keyword. To answer such queries, a tree-based spatial index structure is augmented with approximate-string indexes such as a gram-based inverted index or a trie-based index and resulting structure is called as LBAK-tree index. To answer spatial approximate string (SAS) query, the MHR-treeis proposed, which embeds min-wise signatures into an R-tree [2]. [4] EXPERIMENTAL WORK Each geo-textual object has a point location with latitude, longitude, and a text description. Let D be a geo-textual dataset. Each spatial object o D is defined as a pair (o. ρ, o. φ), where o. ρ is a 2-dimensional geographical point location and o. φ is a text document [6].The performance of creating the R*-IF tree (Figure 1, Figure 2) is studied for the dataset D and the popular spatial keyword query, namely the knn query. The top-k knn queryq = (φ, ρ, k) takes three arguments, where q. φ is a set of keywords, q. ρ is a spatial query point, and q. k is the number of objects to retrieve. The result of knn, q(d), is a set of k objects, each of which covers all the keywords in q. φ. Objects are ranked according to their distances to q. ρ. Formally, o q(d) (( o D\q(D))(dist(o. ρ, q. ρ) dist(o. ρ, q. ρ)) q. φ o. φ). In this paper, four different ways of Implementing R*-IF tree for a geo-textual dataset Dare presented. To evaluate the performance, two different methods are implemented, In the first method R*-Tree and Inverted file are created simultaneouslyi.e. the node of the R*-Tree and Inverted file at the node are created one after the other. In the second method, first complete R*-Tree is created and then Inverted file are created for required nodes of the R*-Tree. Each of thesealgorithms are implemented for loose combination and for tight combinationof R*-IF tree. loose combination has Inverted file only at the leaf node and tight combination has Inverted file at every node of the R*- Tree. 1 creates R*-IF Tree with loose combination and creates Inverted Files for leaf nodes whenever they are created, updated, and split. 2 creates R*-IF Tree with loose combination and creates Inverted Files for leaf nodes only after it has finished creation of R*-tree. 3 creates R*-IF Tree with tight combination and creates Inverted Files for each node whenever it is created, updated, and split. 4 creates R*-IF Tree with tight combination and creates Inverted Files for each node only after finished creation of R*_tree. 1: R*-IF_TREE construction loosecoupling - simultaneous Input: location and text data set D Output: R*-IF_TREE Procedure: Create R* Tree Create inverted file for each leaf node, at the time of Sulbha K. Powar and Ganesh M. Magar 68

5 International Journal of Computer Engineering and Applications, Volume XII, Issue II, Feb. 18, ISSN Creation Update Splitting 2: R*-IF_TREE construction loose coupling - later Input: location and text data set D Output: R*-IF_TREE Procedure: Create R* Tree Traverse R*-Tree and create inverted file for each leaf node. 3: R*-IF_TREE construction tightcoupling - simultaneous Input: location and text data set D Output: R*-IF_TREE Procedure: Create R* Tree Create inverted file for each node, at the time of Creation Update Splitting 4: R*IF_TREE construction tight coupling - later Input: location and text data set D Output: R*-IF_TREE Procedure: Create R* Tree Traverse R*-Tree and create inverted file for each node. [5] RESULTS AND DISCUSSION The four algorithms to create R*-IF tree were evaluated with a Dataset having 1,217 hospital locationsfrom Mumbai and its attributes type of hospital and kind of services. Splitting factors of the node of the R*-Tree were varied from 2 to 14 for evaluating the performance of the algorithm. As the splitting factor of the Node was increased the number of levels were decreased from 24 to 2. Number of nodes also were decreased from 4519 to 74 resulting in decrease of node splitting and total size of all nodes as shown in Table 1. Sr. No. 1 2 Parameter Number of Levels Number of Nodes Table 1: Evaluation with respect to splitting factor Splitting factor Size of nodes Splitting of Nodes Sulbha K. Powar and Ganesh M. Magar 69

6 SPATIAL KEYWORD QUERY PROCESSING: R*-IF TREE IMPLEMENTATION As can be seen from the Table 2, more number of inverted files are created if they are created simultaneously with R*-Tree (algorithm 1 and 3) as compared to the final number of resulting inverted files. It also seen that more number of inverted files are created in tight combination (s 3 and 4) as compared to the loose combination (s 1 and 2). Number of Inverted nodes and lists created are more for tight combination and space requirement is more in case of tight combination, which decreases by increasing the splitting factor of the node. Table 2: Number of Inverted files created/final number of Inverted files Splitting factor / / / / / / / / / / / / / / / / / / / / / / / / / / / /136 Total number of nodes and leaf visited during Geo-Textual knn query are less in tight combination as compared to lose combination, as the search is pruned by checking region and inverted file at each node. If the branch doesn t satisfy spatial proximity and textual relevancy the branch is truncated from the search and there are no false positive hits in tight combination. Table 3 shows the number of internal and leaf nodes visited during Geo-Textual knn query. It is seen that this number is greatly reduced in case of tight combination. Table 3: Total Number of Internal and leaf nodes visited in knn query Loose Combination Tight Combination Splitting factor Sulbha K. Powar and Ganesh M. Magar 70

7 International Journal of Computer Engineering and Applications, Volume XII, Issue II, Feb. 18, ISSN Figure 3: Performance of queries against Loose/Tight Combination and Splitting Factor of the Node Figure 3 depicts performance of knn search with respect to the loose and the tight Combination of R*-IF Tree. Set of 10 queries q = (φ, ρ, k) were executed against each splitting factor for both loose and tight Combination of R*-IF Tree. In Figure 3 interval 0-10 on x-axis represents splitting factor 2, interval represents splitting 4 and so on. Each interval shows the number of comparisons done for each of the 10 queries from the set. As can be observed from the figure 3, the number of comparisons is much less for tightly combined than the loosely combined R*-IF Tree. Hence the query performance increases tremendously with tightly combining R*-Tree and Inverted Files in R*-IF Tree. Figure 4 shows the result of Geo-textual knn query with k =12, where the green marker is query point and red markers are resulting nearest 12 locations satisfying both spatial proximity and textual relevancy. Figure 4: Result of top-12 query Sulbha K. Powar and Ganesh M. Magar 71

8 SPATIAL KEYWORD QUERY PROCESSING: R*-IF TREE IMPLEMENTATION [6] CONCLUSION In this paper, four algorithms for creating R*-IF tree are presented. It is observed from the experimental evaluation that recreating inverted file for every split at every node is not practical as execution time and space requirement is increased tremendously. Space requirement is more for tight combination, but it is compensated by the reduced search time for Geo-Textual knn query. The fourth algorithm, which implements tight combination of R*-IF tree, where IF tree creation is done only after creating R*-Tree is most optimal as compared to the other 3 algorithms presented in this paper. REFERENCES [1] A. Cary, O. Wolfson and N. Rishe, Efficient and Scalable Method for Processing Top-k Spatial Boolean Queries, in SSDBM, [2] F. Li, B. Yao, M. Tang and M. Hadjieleftheriou, Spatial Approximate String Search, IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 6, pp , March [3] G. Cong, C. S. Jensen and D. Wu, Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects, Proceedings of the VLDB Endowment, vol. 2, no. 1, p , August [4] I. D. Felipe, V. Hristidis and N. Rishe, Keyword Search on Spatial Databases, in ICDE, [5] J. B. Rocha-Junior, O. Gkorgkas, S. Jonassen and K. Nørv ag, Efficient Processing of Top-k Spatial Keyword Queries, in SSTD, Minneapolis, MN, USA, [6] L. Chen, G. Cong, C. S. Jensen and D. Wu, Spatial Keyword Query Processing: An Experimental Evaluation, in VLDB, [7] R. Hariharan, B. Hore, C. Li and S. Mehrotra, Processing Spatial-Keyword (SK) Queries in Geographic Information Retrieval (GIR) Systems, in SSBDM, Banff, Alta., Canada, [8] S. Alsubaiee, A.Behm and C.Li, Supporting Location-Based Approximate-Keyword Queries, in ACM GIS 10, San Jose, CA, USA, [9] S. Powar and G. Magar, A REVIEW ON HYBRID GEO-TEXTUAL INDEXING TECHNIQUES, International Journal of Advanced Research in Computer Science, vol. 8, no. 7, pp , [10] X. Cao, L. Chen, G. Cong, C. S. Jensen, Q. Qu, A. Skovsgaard, D. Wu and M. L. Yiu, Spatial keyword querying, in International Conference on Conceptual Modeling, Verlag Berlin Heidelberg, [11] Y. Tao and C. Sheng, Fast Nearest Neighbor Search with Keywords, IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 4, pp , April [12] Y. Zhou, X. Xie, C. Wang, Y. Gong and W.-Y. Ma, Hybrid Index Structures for Location-based Web Search, in CIKM, Bremen, Germany, [13] Z. LI, K. C. K. LEE, B. ZHENG, W.-C. LEE and D. L. LEE, IR-Tree: An Efficient Index for Geographic Document Search, IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 4, pp , September Sulbha K. Powar and Ganesh M. Magar 72