A Novel Cache Management Using GeographicallyPartial Matching for Location-based Services

Chung-Ming Huang and Ming-Sian Lin

Lab. of Multimedia Mobile Networking,Dept. of Computer Science and Information Engineering,

National Cheng Kung University,Tainan, Taiwan, R.O.C.

Email: huangcm@locust.csie.ncku.edu.tw

Wen-Yao Chang

Emerging Smart Technology Institute,Institute for Information Industry,

Taipei, Taiwan, R.O.C.Email: wychang@iii.org.tw

Abstract—In this paper, we propose a new cache managementscheme in the proxy server for location based services. Theproxy server is able to cache query results of location-basedservices (LBS). One of the major LBS is mobile advertising.Mobile advertising is to distribute advertising messages to thetarget users with mobile devices. Nowadays, users are willingto receive mobile advertising messages. Users can issue queriesto search advertisements within specific area. The searchingarea of the user query is seen as a rectangle in this paper. Intraditional cache management, even though the searing areasof different users’ queries are similar geographically, users’queries are still viewed as two different queries. The cacheperformance is low. In this paper, we propose the concept ofthe geographically partial matching for LBS. In geographicallypartial matching, two different queries are viewed as the samequery if their searching areas are similar. As a result, the hit ratein the cache of the proxy server for LBS can be improved. Thesimulation results show that the partial matching can increasethe re-utilization ratio of the cached content dramatically.

Keywords: Location-based services, mobile advertisement, cachemanagement, geographically partial matching

I. INTRODUCTIONThe fast development of wireless technology brings differentlive for us. More and more users are getting used to havea mobile device at hands, such as smart phone, PDA, etc.Location-based services (LBSs) are considered as one of themajor applications for mobile devices. LBSs exploit the user’slocation to provide context-aware services. Currently, severaltechnologies are able to locate the user location dynamically,e.g., GPS.One famous LBSs is mobile advertising. Mobile advertising

is to distribute advertising messages to the target users withmobile devices [1], [2]. Mobile advertising catches the eyesof service providers, operators, and advertisers. Users arewilling to watch and receive advertisements (ads) [3]. Unlikethe traditional e-commerce applications, mobile advertisingoffers context-award, personalized and location-based services.For example, when the dinner time is coming, many peopleare looking for restaurants, which are close to their currentlocations. Restaurants advertises their new discounts using themobile advertising technology. The new menu and discount

are advertised to people around the dinner time. People donot receive advertisement of restaurants, which are far awayfrom his current location.In the above scenario, service providers or operators play an

important role. Service providers or operators are responsibleto push the advertisement to mobile users. Traditionally, themostly used type of ads are SMS (Short Messaging Service)and MMS (Multimedia Messaging Service) [4], [5], [6]. Usersare not able to request ads actively neither using SMS norMMS. As the radically development of wireless technology,many mobile devices are equipped with 3G and Wi-Fi in-terfaces. In other words, mobile devices are able to receivericher content than SMS and MMS. Users are able to searchads based on their current locations actively. In this manner,users also can pull ads from service providers.When users are pulling ads from service providers, they

must give the searching constraints of ads. The constraints canbe location constraints, or other constraints, such as types ofrestaurants. The location constraint contains the user’s currentlocation and the searching area of ads. For example, assumingthat Peter wants to search the ads of Chinese restaurants,which are in the range of 100m X 100m with respect tohis current location during the dinner time. Typically, Peterwill issue the query constraint to the mobile ads server. Themobile ads server queries the database and returns the queryresult to Peter. However, if many people that are in the samebuilding as Peter issue the same query during the dinner time,the mobile ads server needs to query the database frequently.Peter and other persons who are in the same building havea little difference in the geographic location and thus thesearching areas of different persons may be almost the same.The above scenario reflects the fact that the pulling ads iswith two characteristics, temporary locality and spatial locality.For temporary locality, if the mobile ads server receives onequery, then the mobile ads server may receive similar queriessoon. For spatial locality, the mobile ads server would receivequeries, whose searching areas are almost the same.Figure 1 shows the location-based ads searching. George

and Mary are at near but different locations. They all wantto search ads of the Chinese restaurants within the range of

2010 24th IEEE International Conference on Advanced Information Networking and Applications

1550-445X/10 $26.00 © 2010 IEEE

DOI 10.1109/AINA.2010.69

614

Fig. 1. Location-based ads searching.

100m X 100m. Since the searching area of George and Maryhave a little difference, the queries of George and Mary areconsidered as different. In the traditional cache management,the query result of George cannot be applied to Mary.In this paper, the concept of geographically partial matching

is devised for LBS. The geographically partial matchingcompares the similarity of two areas geographically. If thesimilarity of two areas is higher than a given threshold, thetwo areas are said to be geographically partial matched. In theabove example, the searching areas of George and Mary arevery similar and only have a little difference geographically.The two searching areas is geographically partial matched. Thequery results of George and Mary only have a little differencesin the marginal area. If Mary does not care about whether ornot the marginal ads are included in the query result, thenGeorge’s query result can be applied to Mary. Moreover, ifpeople in the same building are querying similar ads in thesimilar areas, query results are applicable for others usinggeographically partial matching. Consequently, the efficiencyof cache in the proxy server can be improved.In this paper, we propose a novel cache management in the

proxy server based on the concept of the geographically partialmatching for LBS. The main concepts are as follows:

• Location-based query: a location-based query is definedfor the cache management. The location-based queryis composed of location constraints and non-locationconstraints. All users’ queries, which are applicationdependent, are translated to the location-based query forfurther processing.

• Geographically partial matching: The geographically par-tial matching algorithm is devised for matching two sim-ilar location-based queries. With geographically partialmatching, two location-based queries whose searchingareas are close can be regarded as the same query. Asa result, the hit rate of cache in the proxy server can beimproved dramatically.

• Cache management for LBS: a cache management thatadopts the concept of the geographically partial matchingfor LBS is proposed. With the cache management, the

region server can act as the proxy server to share theload of the primary mobile ads server.

The rest of this paper is organized as follows. Section IIreviews related works of LBS and mobile ads. Section IIIintroduces the concept of cache management in the proxyserver for LBS. Section IV shows the evaluation results.Finally, conclusion remarks are given In Section V.

II. RELATED WORKS

In recent years, researchers have focused on the mobileadvertisement considering context-aware and user preference.Hristova et al. proposed a context-sensitive advertising system,i.e., Advertising for the Mobile E-commerce user, Ad-me [7].The objectives of Ad-me are context definition and utilization,user and advertising profiling, incorporation of geographicalinformation, device profile considerations, and estimation ofthe effect of advertising on sales revenue. Ad-me offers a valu-able revenue model whereby it is anticipated that consumerswould not pay a subscription charge but rather advertiserswould either: pay a fixed rate a priori charge per month ofadvertising; or pay a percentage of sales revenue accrueddirectly as a result of the usage of the system.Lee et al. analyzed the actual response rate in mobile

advertising [8]. The analysis implies that customers differentlyrespond to mobile advertising messages by content type,time and age, and that new techniques in mobile advertisingcontinue to promote mobile handsets to become a usefuladvertising tool.Mahmoud proposed an agent-based system for provision-

ing context-aware advertisements to wireless mobile usersof J2ME and WAP devices. User preferences and devicecapabilities, which are used in context filtering, are based onthe W3C standardized profile (CC/P) [9].Kim et al. proposed Mobile Advertisement System (MAS)

using the data push scheduling scheme based on user’s contentpreference model [10]. The push advertisement schedulingscheme enables a system to select ad content guided by userpreference model based on consistent characteristics subjectto content category.Rashid et al. proposed a system that can be used with

any current mobile phone system to provide location basedinformation/advertisements to any mobile phone, which isequipped with the Bluetooth technology, without any necessityof installing client side software [11]. The system can be usedto provide systems such as location based information fortourists in cities or museums or location based advertisements.Brasche et al. proposed a location-based caching system

providing the ability for a mobile communication device todynamically load and cache a map related to a user’s location[12]. The system predicts the next map according to themovement of the mobile device. Previously used maps arecached in the local cache of the mobile device. Since mapsare cached in the local device only, the cached data cannot beshared for other users.However, most of the literatures did not consider the cache

management for mobile advertisement. Mobile advertisement

615

Fig. 2. Proxy server architecture.

is highly related to the geographical information. Thus, in thispaper, we focused on the cache management of the proxyserver for mobile advertisement.

III. CACHE MANAGEMENT

In this Section, the proxy server architecture for mobile adsand the concept of the geographically partial matching isintroduced.

A. Proxy server for mobile adsIn the mobile advertisement, ads are stored in the database.

Ads are associated with geographical information and areclassified into different categories. Thus, users can search adsbased on the location constraints and the categories constraints.Figure 2 shows the proxy server architecture for mobile ads.Only one main primary server can connect to the databasedirectly. In each region, there is one proxy server. The proxyserver shares the load of the primary server. The proxyserver caches the results of user queries. Depending on theircurrent location, users with mobile devices can connect to thecorresponding proxy servers. When users want to query thelocation-based information or service, they send query requeststo the proxy server.

B. Location-based queryThe location-based query adopts the geographically partial

matching as the query matching method. The concept ofgeographically partial matching is to find out the similarityof two areas. Thus, several types of areas are used ingeographically partial matching when comparing two areas.Let Q be the searching area of the user query and R be thecovered area that has data being cached to be compared.Figure 3 shows the area definitions. The overlapping area isQ

⋂R. The non-overlapping area is R - Q

⋂R. The ratio

of overlapping area is defined as

the size of Q⋂

R

the size of Q

Fig. 3. Area definitions.

The ratio of non-overlapping area is defined as

the size of R − Q⋂

R

the size of Q

The location-based query is composed of four fields, i.e.,

< LC >< NLC >< MOP >< MNOP >

• LC: Location constraints−The location constraints con-tain the size and the original point of an rectangle, i.e.,(x, y, width, height).

• NLC: Non-location constraints−The other constraints inthe user query.

• MOP : Minimum overlapping ratio−The minimum over-lapping ratio defines the minimum ratio of the area thatis overlapping.

• MNOP : Maximum non-overlapping ratio. The maxi-mum non-overlapping ratio defines the maximum ratioof the area that is non-overlapped.

In the location-based query, the overlapping area is the areais in the searching area of the user query. The overlappingarea should be as large as possible. On the contrary, the non-overlapping area is the area that is out of the searching area ofthe user query. The non-overlapping area should be as smallas possible. The NOP and MNOP define two thresholds forthe geographically partial matching to compare the similarityof two areas. When comparing two areas, i.e., A and B, if Bsatisfies NOP and MNOP of A, then B is geographicallypartial matched with A.Figure 4 shows the steps for processing user queries. A user

issues his query through the user interface. The query handlerreceives and forwards the query to the location-based querytranslator first. The user query is translated into a location-based query. The location-based query translator extracts thelocation constraints in the user query to compose the location-based query. The geographically partial matching is performedto compare with the cached content.In the proxy server, searching results of user queries are

stored in the cache. When storing the searching results, thelocation information must be preserved. Thus, the locationinformation is used for the cache index to improve the searchefficiency. The index format for the cached contents that are

616

Fig. 4. Steps for processing user queries.

composed of three fields, i.e.,

< LC >< NLC >< T >

• LC: Location constraints−The location information spec-ifies the size and the original point of an rectangle, i.e.,(x, y, width, height).

• NLC: Non-location constraints−The other constraints inthe user query.

• T : Timeout−The expire time for the cached content.Basically, the location constraints and the non-location

constraints of the index and the location-based query are thesame. The proxy server extracts the parts of LC and NLCof the location-based query as the index of the content to becached.Figure 5 shows the geographically partial matching algo-

rithm. The geographically partial matching algorithm is to findout a cached content that has the same non-location constraintswith the given location-based query and its covered area isgeographically partial matching with the searching area of thegiven location-based query. Let α be the actual overlappingratio, β be the actual non-overlapping ratio, and γ be theweight of non-overlapping ratio. Both α and β are calculatedfrom the LC of the location-based query and the index of thecache data.Since there may be many cached content is geographically

partial matched with the given location-based query, thedegree of the matching rate is defined to rank the cachecontent that is geographically partial matched. The matchingrate is defined as

(1 − γ)α + βγ

In the geographically partial matching algorithm, the non-location constraints are compared first. The cached contentis evaluated interactively. In the main loop, the non-locationconstraints of the query and the cached content must matchfully. Otherwise, the query and the cached content are consid-ered as different. If the non-location constraints are matched,then the actual overlapping ratio and non-overlapping ratioare computed. The actual overlapping ratio must exceed theminimum overlapping ratio, and the actual non-overlappingratio must not exceed the maximum non-overlapping ratio.

Fig. 5. Geographically partial matching algorithm.

Fig. 6. The control flow char of the location-based query.

If the currently evaluating content has higher matching ratethan the matched content, then the matched content is replacedby the currently evaluated content. If the actual overlappingratio and the actual non-overlapping ratio satisfy the minimumoverlapping ratio and the max non-overlapping ratio respec-tively, then the compared cache content can be used for thegiven location-based query. The algorithm also computes thematching rate of the matched cache content. The cache contentwith the highest matching rate is returned.Figure 6 shows the control flow chart of the location-based

query. The mobile device issues the location-based query to theproxy server. The proxy server first checks its local cache usingthe geographically partial matching algorithm. If the cachedcontent is matched, then the cached content is delivered to themobile device. If no cached content is matched, then the proxyserver redirects the request to the primary server. The primaryserver queries the database and sends the query result to theproxy server. The proxy server forwards the query result tothe mobile device and caches the returned query result in itslocal cache for future use.

617

Fig. 7. Cache hit rate .

IV. EVALUATION

Section evaluates the performance of geographically partialmatching. In the simulation, the map size is 20km X 20km.The searching area of the location-based query is assumedto be 50m X 50m. The average incoming request is 10,000requests per hour. The cache size of the proxy server is 10GB.

A. Cache hit rate

In this subSection, the cache hit rate is evaluated. Inpractice, most of the user queries are searching the same area,which are called the hot zone. In the simulation, the hot zoneis assumed to occupy 10% of the map. The user queries thatsearch the hot zone are assumed to be 80%.The tested cases are as follows: (1) No geographically

partial matching (No GPM) (2) 90% MOP and 10% MNOP,(3) 80% MOP and 15% MNOP, and (4) 70% MOP and 20%MNOP.Figure 7 shows the cache hit rate for different minimum

overlapping ratios. It is obviously that the cache hit rate ispretty poor for No GPM. The cache hit only occurs when thesearching area and non-location constraints are fully matched.If the geographically partial matching is applied, the cachehit can be improved dramatically. Since 70% MOP and 20%MNOP has a loosen constraint, the cache hit rate is highest.The cache utilization is defined as the percentage of the

used cache size. Figure 8 shows the cache utilization. Whenthe cache utilization is low, the cache hit is also low.Many cached contents are hard for reuse without the geo-

graphically partial matching algorithm. The cache in the proxyserver is occupied by these useless data. Even when the cacheutilization grows to 80%, the cache hit rate is not improvedmuch. The cached content has more probability to be usedagain with the geographically partial matching algorithm. Thegeographically partial matching algorithm uses less cache size,but has a higher cache hit rate than the traditional cachemanagement does.

Fig. 8. Cache utilization .

Fig. 9. Cache hit rate in the hot zone.

B. Hot zone

In this subsection, we evaluate the cache efficiency in thehot zone. The user queries that are searching the hot zoneare assumed about 60%, 70%, 80% and 90%. The minimumoverlapping ratio is assumed to be 90% and the maximumnon-overlapping ratio is assumed to be 10%.Figure 9 shows the cache performance in the hot zone. The

normal case means that the concept of hot zone is not applied.Queries that search the hot zone are called hot zone queries.When the number of hot zone queries increased, the cache hitrate was improved. In the normal case, it was surprised thateven though the percentage of the hot zone query increased,the cache hit rate is not improved much.Figure 10 shows the cache utilization. For the normal case,

the proxy server ran out of buffer quickly. The cache utilizationreached the full utilization in 10 hours. However, the cachehit for the normal case was not increased much. When thepercentage of the hot zone queries increased, the used cachedsize was reduced dramatically and the hit rate was improved.For the 90% hot zone queries, the cache utilization only

618

Fig. 10. Cache utilization in the hot zone.

reached 30%, but the cache hit rate was near 80%.

V. CONCLUSION

In this paper, we have proposed a cache management methodusing the concept of the geographically partial matching in theproxy server for location-based services (LBS). Two searchingareas are geographically partial matched if their overlappingand non-overlapping areas satisfy the given thresholds. Thesimulation results show that the geographically partial match-ing can improve the cache hit rate dramatically. Moreover, thelocation-based query is devised for separating the location con-straints and non-location constraints. Thus, the proxy servercan implement the geographically partial matching to increasethe cache hit rate. The main contributions are summarized asfollows:

• location-based query: The user query is translated into alocation-based query. As a result, the location-constraintsand non-location constraints are separated. The proxyserver can utilize the location-constraints to improve thecache efficiency

• High cache hit rate: the traditional cache managementrequire all constraints in the user query must be fullymatched. However, the user query is usually related to thelocation constraints. Based on the location constraints, thegeographically partial matching can improve the cache hitrate dramatically.

• Load sharing: Since the cache hit rate is improved, mostuser queries can be served at the proxy server. The proxyserver can reduce the overhead of the primary server.

Acknowledgment This research is supported by the NationalScience Council of the Republic of China under Grant No.NSC 98-2219-E-006-008 and the Program of Top 100 Univer-sities Advancement, Ministry of Education, Taiwan, Republicof China and Institute for Information Industry.

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