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Processamento de Consultas Espaciais Baseado em Cache Semântico Dependente de Localização

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. Contents. Background Goal and Motivation Related Work

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Processamento de Consultas Espaciais Baseado em Cache Semântico Dependente de Localização

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  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) andXu 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 distantfrom the user’s 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, SR, SP, SA, SC, Sts, SG): • the geographic area SA, • the pointer SC, • the timestamp STS and the group SG. • the name S, • the relation SR, • the selection predicate SP, • This model maintain the spatial information SA, that represents the segment geographic area. Example of the Location-Dependent Semantic Cache Index

  8. Spatial Query Processing • Our query processing model involves two steps: • select the semantic segments candidate set; 1º) SR = QR 2º) SA QJ 3º) QP SP QP: price < 100 S1P: price < 50 S3P: price < 150 S7P: price > 200 CjSC = {S1, S3} Example: “Give me all hotels within 5 miles with diary price lower than U$100”

  9. QP QP QP SP Spatial Query Processing • process the query against each segment and after in the database in the server when is necessary. For each Si in CjSC do { Ii intersection (SiA , QJ) If (QP SiP) { Send to server AQSi in Ii//**QP^SiP APQ  APQ AQSi } Execute Q in Ii APQ  APQ Q X  X + Ii }}//** vector X If X <> QJ then Send to server RQ = Q  ¬X AQ = RQ  PQ QP: price < 100 S1P: price < 50 AQS1: 50<price<100 S3P: price < 150

  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: QP: price < 100 S3P: price < 150

  11. Semantic Segment Formation and Reorganization Remove from Si the content (Si QP in Ii) If Si - (Si QP ) in Ii then Create a new segment S’’ SiA SiA – Ii If SiA < > rectangle form then { Adjust SiA with a rectangle representation } } Predicate Adjust Geographical Adjust Example: QP: price < 100 S3P: 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?heloise@inf.ufsc.br

  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. “SELECT Hotel.nome FROM Hotel WHERE Hotel.diaria < 100 AND ((Hotel.geometria IN QJ) AND (Hotel.geometria NOT IN X))”. Probe and reminder query Geographic Area

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