1 Processamento de Consultas Espaciais Baseado em Cache Semântico Dependente de Localização Heloise Manica Murilo S. de Camargo Cristina Dutra de Aguiar Ciferri Ricardo Rodrigues Ciferri Novembro, 2004

2 Contents Background Goal and Motivation Related Work Location-Dependent Semantic Cache Spatial Query Processing Semantic Segment Formation and Reorganization Conclusion and Future Work

3 Background Mobility has opened up new classes of applications such as Location-Dependent Information Service (LDIS). A location dependent query (LDQ) is a query that is processed on location dependent data, and whose result depends on the location criteria explicitly or implicitly specified (Ren and Dunham,2000). Example: Find the restaurants within 3 miles from my position (implicit location)

4 Goal and Motivation Managing data in LDIS faces challenges (Lee et al., 2002): Low-quality communication; Frequent network disconnections; Limited local resources. Advantage of caching model for mobile computing: Wireless network traffic cost down; System performance up; Reduce power consumed with server communication; Improve data availability in case of disconnection.

5 Goal and Motivation Main goals: Propose a new semantic cache model for LDIS based on relationship between the data and its geographical location; Connects spatial database and mobile computing to location dependent query processing; Propose a solution for semantic segments management and reorganization.

6 Related Work Dunham and Kumar (1998) and Lee et al. (2002) introduced the concept of location dependent data and present new research issues. Zheng et al. (2002) and Xu et al. (2003) studied cache management issues for location dependent data under geometric and cell-based model respectively. Dar et al. (1996) were the first to use the semantic model with distance function. Their replacement policy discard semantic regions that are more distant from the users current location. Ren and Dunham (2000) investigate the semantic caching model to manage location-dependent data, and proposed the replacement policy FAR (Furthest Away Replacement).

7 Location-Dependent Semantic Cache (LDSC) The LDSC index is composed by the tuple (S, S R, S P, S A, S C, S ts, S G ): S ID SRSR SPSP SASA SCSC S ts SGSG S1S1 HotelPrice < 100[(5,15), (15,25)]4T1T1 1 S2S2 Restauranttype = chinese [(10,30), (-30,-10)]8T2T2 1 Example of the Location-Dependent Semantic Cache Index This model maintain the spatial information S A, that represents the segment geographic area. the name S, the relation S R, the selection predicate S P, the geographic area S A, the pointer S C, the timestamp S TS and the group S G.

8 Spatial Query Processing Our query processing model involves two steps: select the semantic segments candidate set; 1 º ) S R = Q R 2 º ) S A Q J 3 º ) Q P S P Example: Give me all hotels within 5 miles with diary price lower than U$100 Q P : price < 100 S 1P : price < 50 S 3P : price < 150 S 7P : price > 200 CjSC = {S 1, S 3 }

9 Spatial Query Processing process the query against each segment and after in the database in the server when is necessary. For each S i in CjSC do { I i intersection (S iA, Q J ) If (Q P S iP ) { Send to server AQS i in I i //**Q P ^ S iP APQ APQ AQS i } Execute Q in I i APQ APQ Q X X + I i } } //** vector X //** vector X If X <> Q J then Send to server RQ = Q ¬X AQ = RQ PQ Q P QPQP Q P : price < 100 S 1P : price < 50 AQS 1 : 50<price<100 S 3P : price < 150 Q P SPSP

10 Semantic Segment Formation and Reorganization Only the data brought into the cache from server should be stored in a new segment. The worst case: Partial geographical relationship Partial predicate relationship Example: Q P : price < 100 S 3P : price < 150

11 Semantic Segment Formation and Reorganization Remove from S i the content (S i Q P in I i ) If S i - (S i Q P ) in I i then Create a new segment S S iA S iA – I i If S iA rectangle form then { Adjust S iA with a rectangle representation } } Predicate Adjust Geographical Adjust Example: Q P : price < 100 S 3P : price < 150 S : price < 100 S : 100 < price < 150

12 Conclusion and Future Work Our proposed model allows the semantic cache management based on spatial property of the cached data. Semantic caching characteristics, spatial query processing strategy and practical issues of semantic caching client management were described. The next step is to investigate the performance of the proposed model. Future studies also will explore semantic cache management issues for more complex spatial location-dependent queries and replacement policy.

13 Obrigado! Perguntas?

14 Spatial Query Processing Problem: The geographic area that it will be searched in the server is a polygon with complex representation. To solve this problem we propose the use of a vector X that stores the rectangle of the areas already searched in cache. Probe and reminder query Geographic Area SELECT Hotel.nome FROM Hotel WHERE Hotel.diaria < 100 AND ((Hotel.geometria IN QJ) AND (Hotel.geometria NOT IN X)).

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