1 / 41

Semantic Query Caching in Mobile Environments

Semantic Query Caching in Mobile Environments. By: Jekkin Shah Advisor: Dr. Konstantinos Kalpakis. Semantic Query Caching in Mobile Environments. Introduction Motivation Contribution Concept of Semantic Caching Issues involved in semantic caching System Architecture

leiko
Télécharger la présentation

Semantic Query Caching in Mobile Environments

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Semantic Query Caching in Mobile Environments By: Jekkin Shah Advisor: Dr. Konstantinos Kalpakis

  2. Semantic Query Caching in Mobile Environments • Introduction • Motivation • Contribution • Concept of Semantic Caching • Issues involved in semantic caching • System Architecture • Prototype and Experiments • Conclusion and further work

  3. Introduction • Disparate works and progresses in : • Geographic Information System (GIS) • Global Positioning System (GPS) • Wireless Technology • Handheld devices • Convergence to Mobile Geographic Information System (mobile GIS) • Rapid growth in mobile GIS applications in all walks of life • Emphasis on spatial data, its storage, retrieval and manipulation

  4. Convergence GIS GPS Mobile GIS Wireless Handheld

  5. Growing List of Applications • Car navigation systems • Emergency services • Real time stock quotes • Field services • Real time tracking and routing of shipments • Environmental surveys and the list is growing rapidly …

  6. Semantic Query Caching in Mobile Environments • Introduction • Motivation • Contribution • Concept of Semantic Caching • Issues involved in semantic caching • System Architecture • Prototype and Experiments • Conclusion and further work

  7. Motivation Hungry !!! Lets find a nearby restaurant query Q1: FIND restaurants WHERE location = “nearby” Found 37 matches

  8. Example 1 (cont.) Wait …We also need some gas !!! Lets see if we can find a gas station near McDonalds. query Q2: FIND “McDonalds” WHERE gas Station = “nearby” Found 2 matches

  9. Shouldn’t we speed up the process ? • Query Q1 is in local cache • Query Q1 subsumes query Q2 • Why do we need to execute query Q2 from scratch ?? • We need a technique to determine and extract Q2 from Q1 • Unfortunately, traditional techniques like page caching do not provide much help in this case Q1 Q2

  10. A new approach – Semantic Caching • Along with query results, store the queries also in cache • Use these queries (query descriptors) to determine if and how a new query can be answered from cache • Check if the required data is present in cache. • Extract the data from cache • Add, remove, merge data by performing corresponding operation on query descriptors • Manage cache by managing the query descriptors • Think of query descriptors as intelligent pointer references that implicitly contain some information about the data they refer to

  11. Problems with traditional caching • Pointer references do not contain any implicit information • Q1  p1,p2,p3,p4,p5,p6 • Q2  p7,p8,p9,p10,p11,p12 • Q3  all the pages • Space constraints will make it difficult to store all the pages in cache. p1 p2 p3 p4 p5 p6 data3 p7 p8 p9 p10 p11 p12

  12. Semantic Query Caching in Mobile Environments • Introduction • Motivation • Contribution • Concept of Semantic Caching • Issues involved in semantic caching • System Architecture • Prototype and Experiments • Conclusion and further work

  13. Contribution • An architecture for Semantic Caching in mobile environments • A system prototype as a “proof-of-concept” with the following building blocks • Query parser and validator • A Solver for determining query satisfiability • An Executor for processing partial and remainder queries • A Cache manager for efficiently managing the cache • A cache replacement algorithm • Techniques for query processing

  14. Semantic Query Caching in Mobile Environments • Introduction • Motivation • Contribution • Concept of Semantic Caching • Issues involved in semantic caching • System Architecture • Prototype and Experiments • Conclusion and further work

  15. Issues in semantic caching • Although the idea of semantic caching is straight forward, store query descriptors along with their results, the issues involved are much harder !! • Simple concept but Difficult Implementation Issues • 1. We need to decide if the answer is present in cache • 2. If present, do we have sufficient information to extract it ?

  16. Answering Queries from Cache Is result of Q3 present in (Q1 + Q2) ?

  17. Solving the implication problem Let T = { Q1, Q2 } be a set of query descriptors already in cache We need to show that QT We show that ¬ (Q  T) is FALSE • ¬ (Q  T) •  ¬ (¬ Q  T) •  Q  (¬T) •  Q  ¬(T1  T2  T3  T4) •  Q  (¬T1)  (¬T2)  (¬T3)  (¬T4) • This is the primary technique used in our thesis. • The algorithm is adopted from [LY85].

  18. Solving the implication problem (Cont.) • Exponential growth in the number of equations to be solved. • Solution: • Clustering based on Signatures • Signature created by taking into account the predicate attributes present in the query • Restriction on the number of clusters created • Signature used in indexing the query descriptors Attr A, B Attr X, D

  19. Data Extraction problem Can we extract Data3 ? Data1 Data3 Data2 We fetch attribute C from remote source and take a Cartesian product with the data already present in cache

  20. Answering Partial Queries • What happens if QT is FALSE ? • There may be a non empty intersection set between Q and T • Answer (Q T) locally (Partial match) • Send (Q ¬ T) to the server (Remainder Query) T1 T2 Q

  21. Semantic Query Caching in Mobile Environments • Introduction • Motivation • Contribution • Concept of Semantic Caching • Issues involved in semantic caching • System Architecture • Prototype and Experiments • Conclusion and further work

  22. Semantic Caching Architecture Solver (Query implication) query Query parser and Validator Executor Remote db results Cache manager Local Cache

  23. Cache Structure • Local Cache is implemented as relational database structures • Query descriptors are stored in one table indexed by their signatures • Corresponding query results (data) are stored in another table • An auxiliary table associates the query descriptors with its corresponding data • Cache manager interacts with query descriptor table • Manipulation of data is achieved through the manipulation of query descriptors

  24. Cache: Operations and Management • Cache Manager: • Replacement module: • Replacement: Determines what needs to be cached and what can be purged out • Management module: • Addition: Granularity of addition is a semantic region • Deletion: Removal of region, though not necessarily leading to the removal of data • Merge: To simplify query processing, two or more regions can be merged • Decomposition: A very large region, can be decomposed for efficiency reasons

  25. Cache Replacement Theory and Assumptions • What is the performance metric ? • Conventional caching schemes optimize one or more of the following parameters with the goal of improving the performance • Hit ratio • Response time • Data transmission time • Due to the dynamics of our application domain, none of these parameters truly reflect the performance of our applications

  26. Theory and Assumptions (Cont.) • Cache Hit Rate : how do we define hit rate ? • One: At least one data record obtained from cache • All: All data records to be obtained from local cache • Mid: 50% of data records to be satisfied from local cache • Response time: • Partially answered queries make it difficult to accurately define the response time • Data transmission time: • Lot of dependence on the actual network parameters like latency and bandwidth

  27. Theory and Assumptions (Cont.) • Mobile environments: Premium on bandwidth • Our goal: “To minimize the cost of servicing the requests that cannot be answered from the local cache” • Cost is measured in terms of time • Performance metric is Byte hit rate (BHR): • Ratio of actual amount of data served from local cache to the amount of data transferred from the remote source • Assumptions: • Negligible query execution time • Uniform latency and bandwidth across the network

  28. Replacement Algorithm • Guiding Action Selection function (GAS) to assign a value to each semantic region • GAS value = a + (s * f * b) • s = size of data transferred from the remote source • f = frequency of access of the query • a, b are domain specific parameters • a = freshness count of each query • b = 1/Sd, where Sd is the distance between the current location of the moving object and the location of query • Using the GAS function the value of each semantic region is calculated

  29. Replacement Algorithm (Cont.) • For each query in cache we have, • GAS value (Vi) • Weight (Wi) • Also, we have a limit on the total size of the cache (W) and also the total number of queries (K) that can be admitted • Problem definition: • “Given a set of rectangles with a weight and a value, choose at most K rectangles that gives maximum value, provided the weight does not exceed W” • The problem can be formulated as the 0-1 Knapsack problem with additional cardinality constraint

  30. Semantic Query Caching in Mobile Environments • Introduction • Motivation • Contribution • Concept of Semantic Caching • Issues involved in semantic caching • System Architecture • Prototype and Experiments • Conclusion and further work

  31. Experiments (Setup) • Requirements • Workload (datasets and queries) • Modeling the behavior of the moving object • Query execution guidelines • Real datasets • Hard to obtain • Complexity in processing due to complex structures of spatial objects • Synthetic dataset generator • Easily generated • Various parameters can be controlled

  32. Workload • Query load selection • Tables • Restaurants: LocX, LocY, Name, ID, tables, City, Zip • Gas Stations: LocX, LocY, Name, ID, Low, Mid, High • Query specifications: • Rectangular queries (select and project only) • Number of queries issued per trip : 20-70 • Type of queries: Location aware, location dependent and non-location related • Frequency of issuance: Selected randomly ranging from 5 ms to 100 ms • Overlap rate: 10-25%

  33. Experiments (Moving Object) • Behavior of Moving Object • Generating Spatio-Temporal Dataset (GSTD) [PT00] • Moves in a 2D space • Static points and regions called infrastructure emulate real life objects like buildings, rivers, roads etc. • Trajectories are generated using specific guidelines • Initial statistical distribution of infrastructure objects • Source and destination location • Speed of moving object • Direction of motion • Duration of journey

  34. Query Execution Guidelines • Controllable parameters • Type of queries: • Location dependent, Location aware, Non-location related • Frequency of query issuance • Selectivity of chosen queries • Query overlap rate • Parameters are chosen in a variety of combinations • Random • Gaussian distribution • Skewed distribution

  35. Results • Cache Size Vs Hit Rate ( NEW vs m-LRU) • The NEW replacement scheme compares roughly equal to modified LRU replacement scheme • BHR increases upto 70% when cache size is progressively increased

  36. Results • Hit rates Vs Number of queries (NEW scheme) • Increasing the number of queries in the system does not substantially increase the hit rates. • Byte hit rate performs nearly equal to Hit rate Mid

  37. Semantic Query Caching in Mobile Environments • Introduction • Motivation • Contribution • Concept of Semantic Caching • Issues involved in semantic caching • System Architecture • Prototype and Experiments • Conclusion and further work

  38. Conclusion • No assumption made on Spatial Locality of Reference • Query descriptors act as “Intelligent” References • Can support Content Based Reasoning • Ability to take advantage of Schema Knowledge • Page / Tuple caching schemes do not scale well in our GIS domain • Reasons: • “Unintelligent” pointer references • Questionable assumption of Spatial Locality of Reference • Inability to take advantage of Semantic Overlaps

  39. Advantages of Semantic Caching • Benefits of Semantic Caching • Leverages semantic locality found in typical mobile GIS applications • Adapts dynamically to the patterns of user queries rather than caching static clusters of tuples • Minimizes cost of cache lookup due to compact representation of query descriptors • Capable of providing partial and/or approximate answers to queries quickly

  40. Conclusion (Cont.) • Shortcomings of Semantic Caching • Complicated cache management schemes • Too restrictive. Solver can process only simple type of queries • Captures the semantics of the query and not the result objects. Hence, fails to utilize cached objects when the semantics of the query do not match

  41. Conclusion (Cont.) • Future work … Lots of things • Make the solver more general to handle different types of queries • Make the caching scheme flexible enough to capture the semantics of the query descriptors as well as the result objects • Simpler cache management • Ability to share cache with peers

More Related