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Adaptive Query Processing in Mobile Environment

Hocine GrineUniversity of Valenciennes

59313 Valenciennes cedex 9,France

hocine.grine@univ-valenciennes.fr

Thierry DelotUniversity of Valenciennes

59313 Valenciennes cedex 9,France

thierry.delot@univ-valenciennes.fr

Sylvain LecomteUniversity of Valenciennes

59313 Valenciennes cedex 9,France

sylvain.lecomte@univ-valenciennes.fr

ABSTRACTThese last years, the evolution of nomadic terminals and mo-bile networks has yield to the development of the ubiquitouscomputing. In this context, actual query evaluation and op-timization techniques in distributed databases based on theuse of a global schema and a cost model are no more rele-vant. Furthermore, a query processor deployed in this typeof environment must face a significant heterogeneity, in par-ticular mobile terminals, communication networks and dif-ferent data sources. In order to cope with this heterogeneity,such service should have the capability to adapt itself dy-namically. In this paper, we describe the problems relatedto query processing in mobile environment and the needs foradaptability.

Categories and Subject DescriptorsC.2.4 [Computer-Communication Networks]: DistributedSystemsdistributed applications; H.2.4 [Database Man-agement]: Systemsquery processing; H.3.4 [InformationStorage and Retrieval]: Systems and Softwaredistributedsystems

General TermsQuery Processing, Mobility, Adaptability

KeywordsAdaptive Query Processing, Mobile Computing

1. INTRODUCTIONToday small notebook PCs, PDAs (Personal Digital Assis-tants) and smart phones are becoming more and more smalland portable making possible the access of digital informa-tion any time, any where [22]. Mobile terminals are commu-nicating and interacting with other terminals through wire-less networks like WiFi or Bluetooth.

Mobile devices can spontaneously network with one anotherwithin their proximity and execute proximity applications.

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise,torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.MPAC 05, November 28- December 2, 2005 Grenoble , FranceCopyright 2005 ACM 1-59593-268-2/05/11... $5.00

These devices will become both sources and consumers ofdata, and can interact with other devices in order to per-form individual or collective computing. In such mobile adhoc environment, elements of the network are very dynamicand can be extremely volatile. For example, a mobile userlooking for a restaurant will obtain different results based onthe time and the place he issued the query. As the locationof other devices changes with respect to other entities anddata sources are constantly in movement it may not be pos-sible to collect information about available data sources atany given point of time. Traditional query processing tech-niques based on global data schema and collected statisticsand histograms are no more adequate in such highly mo-bile environment for many reasons such as: terminals het-erogeneity ranging from a PDA with limited resources andenergy to puissant servers, data types (time, place, IDs ofphysical entities), communication type that can be a wirednetwork with a higher bandwidth or a wireless network witha limited bandwidth. Furthermore, query optimization andexecution must take into account available resources in or-der to effectively evaluate queries. For querying in such en-vironment, query processors shall have the capacity to face

these constraints and adapt themselves in order to ensurea QoS that conforms user preferences terminal constraintsand network characteristics.

The remainder of the paper is organized as follows. In Sec-tion 2, we introduce mobile computing systems. In Section3 data management challenges in mobile environments aregiven. Section 4 defines the current work on query process-ing. Section 5 defines adaptive query processing in mobileenvironment. Finally, a conclusion is given in Section 6.

2. MOBILE COMPUTING SYSTEMS2.1 Mobile Computing Environments

The number of small devices like PDAs, smart phones andsensors grows rapidly carrying with them different sort ofdata. The access to computing and communications is nec-essary not only from the local one, but also while the useris moving from one place to another.

Mobile computing deals with the mobility of hardware, dataand software in computer applications. It is a specializedclass of distributed systems where some nodes can disen-gage from joint distributed operations, move freely in thephysical space and reconnect to a possibly different segmentof a computer network at a later stage in order to resumesuspended activities.

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The goal of mobile computing is to permit users and pro-grams to be as effective as possible in this environment thatis characterized by uncertain connectivity and heterogeneity,without changes to the manner in which they operate.

Figure 1 shows the existing and widely architectural modelof a system that supports mobile computing. This architec-ture consists of stationary and mobile components. The only

mobile component is the mobile unit. A Mobile Unit is amobile computer which is capable of connecting to the fixednetwork via a wireless link. Stationed hosts are connectedtogether via a high-speed network (Mbps to Gbps). Com-ponents in the fixed network are either Fixed Hosts that arenot capable of connecting to a mobile unit, or Base Stationswhich are computers capable of connecting with a mobileunit and are equipped with a wireless interface (they arealso known as Mobile Support Stations) .

crossing

Wireless LAN Cell

Wireless Radio Cell

HostFixed

BaseStation

HostFixed

HostFixed

BaseStation

HostFixed

BaseStation

HostFixed

Station

HostFixed

MobileUnites

WirelessLinks

disconnected

Base

Wireless Radio Cell

Wireless Radio Cells

Hight Speed Wireful Network

Figure 1: Mobile Computing Architecture

2.2 Mobile Computing ApplicationsMobile applications can be categorized depending on whetherthey use fixed or radio communication services. Nomadicsystems are typically based on wired dial-up, or local areanetwork communication facilities. Mobility is not transpar-ent, requiring a new connection to be explicitly establishedwhen the user moves to a new location.

Mobile systems use wireless technology for transparent com-munications while travelling in a train, car, plane or evenwhile walking. During the course of a connection the radioreception is likely to vary considerably.

Location-aware services and applications require informa-tion on a users geographical location in order to display aposition on a map or provide information. This requires ageneralized positioning solution, to track the current posi-tion of the user. This can be accomplished using GPS, cel-lular telephone base stations, active badges, or determiningwhich fixed computer is being used [7].

Mobile computing devices may also need access to local

servers supporting electronic mail, printing, file service ordatabases. This could imply the need to migrate resourcesfrom the users home servers to local ones, rather than justmaintaining network connections to the home servers, in or-der to reduce communication costs.

2.3 Mobile Computing CharacteristicsMobile computing environment has some characteristics that

make the system unique and fertile area of research. Themain issues in mobile computing introduced by [5] are mo-bility, wireless communication and portability:

2.3.1 MobilityThe location of mobile units is an important parameter whenlocating a mobile station that may hold the required dataand when selecting information especially for location de-pendent information services. In this case the search cost,to locate mobile units, is added to the cost of each commu-nication involving them.

2.3.2 Wireless Medium

Wireless networks offer a smaller bandwidth than a wirednetworks; the first ones offer a bandwidth that varies be-tween 9 and 14 Kbps. while any Ethernet offers a bandwidthof 10 Mbps. However, there is an asymmetry in the com-munication because the bandwidth for the downlink com-munication (from servers to clients) is much greater thanthe bandwidth for the uplink communication (from clientsto servers).

Mobile environment is subjected to frequent disconnections.These disconnections can be classified in two types: (i) forceddisconnections, which are usually accidental and unavoid-able, like for example those disconnections that take placewhen entering an out-of-coverage area. (ii) Voluntary dis-

connections, that take place when the user decides to dis-connect his unit with the goal of saving energy.

2.3.3 Portability of Mobile ElementsThe design of portable computers implies that they mustbe small, light, consume little energy, etc. This causesthese computers to have generally more limited functionali-ties than fixed hosts, mainly in aspects such as computationpower, storage capacity, screen size and graphic resolution,autonomy, etc.

2.4 Adaptability in Mobile ComputingThe variable supply of resources, as well as the differing de-mands on them, suggest that the client must adapt to thesechanges. However, this broad notion of adaptation requiresto be defined. In what sense does a mobile system adapt?Which parties in the system are responsible for adaptationdecisions?

Three models of adaptation are being discussed below:

1. Application-Transparent Adaptation: In the firstmodel of adaptation, the system is wholly responsi-ble for adapting to changes in the supply of and de-mand for resources. The system automatically handles

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changes in connectivity between hosts, and transpar-ently decides when to propagate updates or invalidateand re-fetch stale data. Individual applications haveno say in how to make use of available bandwidth,though applications in either system can provide spe-cific functionality, such as conflict resolution.

2. Laissez-Faire Adaptation: At the opposite end ofthe spectrum, applications are solely responsible forcoping with the consequences of mobility. This ap-proach, referred to as laissez-faire adaptation, has beentaken by commercial software such as Eudora. In suchsystems, applications monitor the availability of re-sources, and make their own adaptation decisions inisolation of other applications or the system.

3. Application-Aware Adaptation: The middle groundbetween these two extremes is a collaborative effort be-tween the system and the application [14]. The natureof this partnership is a consequence of end-to-end con-siderations. The system is best positioned to knowwhat is available to the mobile client. Thus, it is re-sponsible for monitoring resource availability, enforc-

ing resource allocation decisions, and optimizing theuse of client-wide resources. An individual applica-tion, however, is the only party which can know fullywhat its own needs are. Hence, an application mustbe informed by the system of significant changes in theavailability of resources, and react to those changes inwhatever way it sees fit. Application-aware adaptationis the only adaptation model that can support the sortof mobile computing.

3. DATA MANAGEMENTIN MOBILE COM-

PUTING SYSTEMSA mobile computing system can be viewed as a dynamic

type of distributed system where links between nodes in thenetwork change dynamically. Figure 2 (adapted from Fig-ure 3 in [4]) shows the classification of database systems interms of four orthogonal axes, i.e., autonomy which refers tothe distribution of control, not of data. It indicates the de-gree to which individual DBMSs can operate independently.Whereas autonomy refers to the distribution of control, thedistribution dimension deals with data, more precisely thephysical distribution of data over multiple sites. Hetero-geneity may occur in various form in distributed systems,ranging from hardware heterogeneity and differences in net-working protocols to variations in data managers. Mobilitydeals with clients that are issuing queries, servers that re-ceive queries, or data targeted by a query.

3.1 Data Management Challenges in Mobile

EnvironmentsMobile environment entities are treated as information repos-itories, we can describe this model as a type of mobile dis-tributed databases. The system is highly autonomous sincethere is no centralized control on the individual databasesthat client maintain. It is also heterogeneous since it mayinclude different types of hardware and different data types.Distribution is also another aspect of ad-hoc networks, whereparts of data reside on different computers. Mobility is ofcourse given, every entity can change its location and there

Autonomy

Heterogeneity

DBMS

Mobile

DBMS

HeterogeneousDBMS

DistributedHeterogeneousMultidatabaseSystems

HeterogeneousMultidatabaseSystem

DistributedMultidatabaseSystem

MobileMultidatabaseSystem

DistributedHomogeneousDBMS

Distribution

HomogeneousMobile

Heterogeneous

Distributed

Mobile

Figure 2: A Classification of Database Systems

is no requirement that some nodes must be fixed in a net-

work.

There exists a considerable number of paper discussing gen-eral issues and research challenges related to mobility [8, 9].Challenges engendered by mobility concern mainly : trans-actions, query processing, replication, caching and security.Mobile environment imposes the following issues that areprimarily related to the randomness of every devices neigh-borhood at any instance of time. The neighborhoods consistof all reachable devices that a particular device can commu-nicate with all available data that is accessible at that time.The main problems are [13]:

3.1.1 Spatio-temporal variation in data source avail-

abilityEntities in a mobile environment are moving and so theirneighborhood. Hence, depending on the specific locationand time a particular query is issued, the originator mayobtain different answers or none at all. Furthermore, datasources availability may vary with location and time.

3.1.2 Absence of a global catalog and schemaAs the neighborhood changes dynamically, a mobile devicehas no prior knowledge of the current set of available data.Some entities in the mobile environment have limited capa-bility, and can not perform schema translation.

3.1.3 Reconnection is not guaranteedWhen a device moves away from a current neighborhoodit may affect any ongoing interaction among other devicesof that neighborhood. An inconsistent global state may becaused by the uncertainty that the mobile devices will everagain be able to communicate among themselves.

3.1.4 The query may be explicit or implicitIn a ubiquitous environment, some interactions occur with-out explicit human intervention. But, in an ad-hoc environ-ment, some entities are able to accept queries from humansand propagate them in the ad-hoc network.

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3.1.5 CollaborationIn mobile environment entities interact in random mannerwhich implies that privacy and trust issues should be takeninto account. There are three main issues that must be con-sidered. First, there may be an entity that has reliable in-formation but refuses to make it available to others. Second,there may be an entity that is willing to share its informa-tion; however, that information may be unreliable. Lastly,

when an entity makes information available to another en-tity, questions regarding protection of future changes andchanging of that information arise.

4. CURRENT WORK ON QUERY PROCESS-

INGQuery processing deals with designing algorithms that ana-lyze queries and convert them into a series of data manipu-lation operations. The main function of a query processor isto transform high-level query (typically, in relational calcu-lus) into an equivalent lower-level query (typically, in somevariation of relational algebra). Figure 3 shows the classicarchitecture for query processing. This architecture can beused for any kind of database system including centralized,distributed, or parallel systems. The query processor re-ceives a query as input, translates and optimizes this queryin several phases into an executable query plan, and executesthe plan in order to obtain the results of the query.

Parser

Query Rewrite

Query Optimizer

Code Gen.

Plan Refinement/

Query Execution

Engine

(Meta Data)

Catalog

Data Base

representation

representationinternal

internal

plan

optimal

executionplan

Query

Result

Compilation

Execution

Figure 3: Phases of Query Processing

4.1 QoS Aware Distributed Query ProcessingIn a dynamic environment, several problems arise. Amongthem we can quote: congestion failures, unpredictable com-munication network, lack of knowledge about the load andpotential delays at remote end systems, varying users re-quirements and expectations from service provider.

One feasible way to capture these changes is to resort toQoS management [26]. Because QoS management aims atdeciding and controlling if and how data streams can be de-livered to the user within the given delay, cost or qualityconstraints, then the following two quality criteria are con-sidered: (i) the cost of a service request , (ii) the total delayfor obtaining the response.

To keep track of the current dynamic performance infor-mation about the underlying network, it is important forthe optimizer to be customized to various environments andapplication requirements. Furthermore, revised or new costmodels (particular communication cost) should be built. Twomodules are added to the conventional query processor : onethat provides different optimization criteria, the other pro-vides the dynamic information for the underlying network.

The architecture is based on the adoption of user profilegenerated from QoS requirements, QoS profile mappingmodule is used for this conversion. Finally, a QoS moni-toring function is invoked during the optimization and theexecution phase.

4.2 Adaptive Query ProcessingIn a distributed environment, data may be distributed overseveral site with different workloads. Thus, statistical in-formation about the available data sources may be minimaland the query optimizer will not have at compile time nec-essary statistics, good selectivity estimates to produce anoptimal query plan. Therefore, traditional optimizers can-not predict the future availability of resources. To face this

problems, Adaptive Query Processing is introduced. In thatcase the query processor adapts itself to changing environ-mental conditions at runtime.

A query processor is considered to be adaptive if it receivesinformation from its environment and determines its behav-ior according to that information. This action is repeatedin an iterative manner, i.e., there is a feedback loop be-tween the environment and the behavior of the query pro-cessor system [6]. Adaptive Query Processing (AQP) makesquery processing more robust to optimizer mistakes, un-known statistics, and to changes in conditions over the run-ning time of a query.

There are mainly six areas of interests where the adaptivequery processing tries to adapt to:

1. Fluctuation in Memory: systems in this category tryto adapt to memory shortages and to the availabil-ity of excess memory. Adapting to memory fluctua-tion can be done by (i) choosing operators like thehybrid hash join algorithm or memory adaptive sort-ing, (ii) scheduling the execution of a query, (iii) queryre-optimization by changing the query plan.

2. User Preferences: adapting to user preferences includescases where users are interested in obtaining some par-

tial results of the query quickly. The user may alsoclassify the elements of output in term of importance.

3. Data Arrival Rates: systems in this category adaptto data arrival rates in parallel and distributed sys-tems, where the response times of remote data sourcesare less predictable. Parachute queries [3] can be usedto adapt to data source failures. Query scrambling[20] and adaptive operators like Xjoin [19] and dou-ble pipelined hash join [24] are used to adapt to slowor bursty transfer rates. Eddies [1] is an example ofan operator that adapts itself to continuous queries indata stream systems.

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4. Actual Statistics: in some cases it is not possible togather accurate statistics about the data sources. Thus,statistical information are collected at runtime in or-der to change the query plan and to adapt the queryexecution.

5. Fluctuation in Performance: performance can be af-fected when a node faces problems like high memoryand CPU load, poor data layout on disks, or compet-ing data streams.

6. Any changes in the Environment: some techniques canadapt to many kinds of changes in their environmentby combining elements of other categories, i.e., to com-puter resources, like memory and processor availabil-ity, and to data characteristics, like operator costs, se-lectivities and data arrival rates.

4.3 Levels of AdaptabilityThe techniques of query evaluation can be classified intothree categories: static query evaluation, personalized queryevaluation and dynamic query evaluation. Thus, accordingto this classification , three levels of adaptability can be

observed, namely : static, personalized and dynamic [21].

4.3.1 Static AdaptabilityThe first level of adaptability is observed during the designand the development phases of the query processor. Staticadaptability mainly concerns the generation of query opti-mizers or extensible DBMS. This tool is used to constructnew query optimizer using two methods: (i) by extendingthe search space where the optimization is based on rulesthat represent all possible manipulations of query plan, (ii)by extending search strategies, where a certain number ofsearch strategies is proposed in order to develop a queryprocessor.

4.3.2 Personalized AdaptabilityAfter the development of the query processor, this one isused by different users with different needs and preferences.Personalization in DBMS appears principally at the level ofdata manipulation language. The aim of personalization isto adapt the choice of query execution strategy to the usersneeds and preferences e.g., users may send a top N query orwants to have results as fast as possible.

4.3.3 Dynamic AdaptabilityQuery optimizer uses information collected from the envi-ronment in order to decide which query plan to be exe-cuted. However, these information may change at runtime

and thus imply query optimizer errors. To face this prob-lem, it is important that the query execution strategy mayhave the ability to adapt to environment changes. Dynamicadaptability aims to rectify incorrect estimations establishedat compilation time due to incorrect statistics or simplifiedcost metrics; it has been applied to long-running continuousqueries.

5. ADAPTIVE QUERY PROCESSING IN MO-

BILE ENVIRONMENTConsider an urban area with several vehicles where driversand passengers in these vehicles are interested in information

relevant to their trip [25]. For example, a driver would likehis vehicle to continuously display on a map, at any time,the available parking spaces around the current location ofthe vehicle. The query processor uses GPS coordinates tosend queries like find the closest parking space. Whenthe driver arrives to a parking space, GPS signals are lessdependable in indoor environments such as brick and mor-tar retail spaces, our driver will look for a closest parking

place. The application looks for another positioning sys-tem like ISLANDS [18], APS [12] or RADAR [2] in orderto fulfill the users query. We assume that two vehicles cancommunicate with each other when their distance is smallerthan a threshold. This communication can be enabled by alocal area wireless protocol such as IEEE 802.11, Ultra WideBand (UWB) or Bluetooth. With inter-vehicle communica-tion, a mobile user discovers the desired information fromthe vehicles it encounters, or from distant vehicles by multi-hop transmission relayed by intermediate moving vehicles.When a vehicle leaves a parking slot, it informs other vehi-cles that its occupied place is henceforth free but only for acertain period of time. Drivers can ask queries like find theclosest free slot within 20 meters or find a nearby placewhere two slots are free or find a slot that will be free inthe next 5 minutes.

Figure 4: ParkMe application

From the previous scenario, we identify several factors thataffect query processing in a mobile environment, and are di-vided into three classes [5]: portability, mobility, and wire-

less communication.

5.1 The Impact of Mobility On Query Pro-

cessingThe effects of mobility on query processing require that al-gorithms employed must be capable of managing frequentloss and appearance of mobile device in the network, andthat overhead should be minimized during periods of lowconnectivity. In this environment we can distinguish manycharacteristics that are listed in the next sub sections.

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5.1.1 Different Location ModelsLocation-based applications require a well-formed represen-tation of spatial knowledge. Current location models can beclassified into symbolic or geometric models. In geometricmodel locations are specified as an n-dimensional coordinate(longitude-latitude pair) or a set of coordinates defining anareas bounding geometric shape (such as a polygon). Sym-bolic models refer to a location by some abstract symbols.

Such a representation allows a reference to a place simplyby abstract symbol or name, which makes it very convenientfor human interaction.

5.1.2 Different Query TypesThe mobility in a mobile environment introduces three typesof entities: (i) mobile client that submits a query, (ii) mobileserver that processes a query or a part of it, and (iii) mov-ing object which represents the data targeted by the query.According to these entities queries can be classified into fourcategories [10]:

1. Location Dependent Queries: in this category three

types of queries are present [15]: firstly, if all the pred-icates and attributes used in a query are non locationrelated then it is called a Non Location Related Query(NLRQ). For example: select all restaurants withItalian specialty. Secondly, if a query has at least oneLocation Related simple predicate or one Location Re-lated attribute then it is called Location Aware Query(LAQ). For example: How is the weather in Lille?.Finally, if a query results depend on the location of thequery issuer then the query is called Location Depen-dent Query (LDQ). For example: Find me the closesthotel within 5 miles of my current position.

2. Moving Object Database Queries: this type of queries

includes all queries issued by mobile or fixed termi-nals and querying objects which are themselves mov-ing. For example a query of this type could be : Findall the cars within 50 feet of my car [17, 16].

3. Spatio-Temporal Queries: in a mobile environment,answers to user queries can vary with location [11].That is, query results depend on a querys spatial prop-erties. For a location-bound query, the query resultmust be both relevant to the query and valid for thebound location. The spatio-temporal type includesall queries combining space dimension with time di-mension which generally associated to moving objects.The time notion introduces two sort of spatio-temporalqueries: the first one considers trajectories describinga time history of the object movement. The secondone focuses on the current position of the moving ob-

ject and possibly its future position.

4. Continuous Queries (CQ): continuous queries areanother type of queries that allow users to receive newresults when they become available. For example adriver is asking for gas stations within 10 miles fromits position. The result of the query is a set of gasstations that varies continuously with the movementof the driver [17].

5.1.3 Query OptimizationQuery optimization methods try in general to obtain exe-cution plans which minimize CPU, input/output and com-munication costs. In centralized environments the cost thataffects most is the input/output whereas in distributed en-vironments, communication cost is the most important. Ina mobile distributed environment, the communication costsare much more difficult to estimate because the mobile host

may be situated in different locations. The best site fromwhich to access data depends on where the mobile computeris located. In general, it is not worth calculating plans andtheir associated costs statically, but rather, dynamic opti-mization strategies are required in this mobile distributedcontext.

5.1.4 Query ExecutionIn static systems, query processing execution sites are de-termined in advance, i.e., which steps are performed on theclient and which one on the server. In a mobile environ-ment, where users are moving, such assumption is inad-equate. Thus, mobile database systems must be able tochoose an execution site for the different phases of query pro-

cessing depending on their current environment and shouldbe able to revise that decision as flexible as possible.

5.2 The Impact of Portable Devices Limita-

tionsIf we reference dynamic location information in a query, wehave to use a location management component to get thisinformation. Thus, depending on the offered localizationstrategy, we have different possibilities to use this informa-tion. The cost evaluation of a query execution plan is guidedby required resources of the plan. The main factors that areused in stationary systems are CPU-usage and the numberof hard disk access. In mobile systems, additional vary-

ing factors like energy consumption, available memory andCPU-speed may be included.

5.3 The Impact of Wireless CommunicationThe new networking technologies allow spontaneous connec-tivity among mobile devices, including hand helds, comput-ers in vehicles, computers embedded in the physical infras-tructure, and (nano)sensors. Mobile devices can suddenlybecome both sources and consumers of information. Thereis no longer a clean distinction between clients and servers,instead devices are now peers. Furthermore, there is nolonger a guarantee of infrastructure support. Consequently,for obtaining data, devices cannot simply depend on a helpof some fixed, centralized server. Instead, the devices must

be able to cooperate with others in their proximity in orderto pursue individual and collective tasks.

5.4 The Need for AdaptabilityWe listed above the main obstacles that a query processorcan face in a mobile environment. More specifically, in an adhoc environment mobile devices are highly volatile, meaningthat the time of a connection in the network is not known.Furthermore, as mobile devices are moving and as new datacan arrive at any moment, there is no guarantee about thetype of information available at any given time and spaceand the answer to a query issued by a mobile user could

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be sent to a location that is already left. To support dis-tributed query processing in mobile network, disconnection,bandwidth and reliability must be considered and query op-timization must take these characteristics into account andadopt appropriate optimization strategies based on the sta-tus of the network.

Resource limitations create asymmetry between mobile el-

ements. In [23] three feature of asymmetry were identi-fied, namely: asymmetry feature of computing capabilitybetween the server and mobile computer, asymmetry fea-ture of energy consumption between message sending andreceiving, and asymmetry feature of energy consumption be-tween activeness and idleness of mobile computer. [23] hasexamined three different join methods and developed threequery processing schemas. These features can be easily iden-tified in a mobile ad hoc network, where mobile devices canplay the role of client and server. Thus, the query processorshould be able to choose the adequate schema in order tocope with energy problems.

Mobility of users affects mostly the network topology andthe location of mobile devices that can be seen as a fre-quently changing data. In this case query processor mayhave to treat continuous data (in our case location) andcontinuous queries that may include a temporal dimension.For example a stationary driver may issue a query and wantsto locate the closest free parking slot, then in the course ofhis driving he may issue a query and asks for the closestfree parking slot. Thus, the query processor will have totreat first a snapshot location dependent query (in station-ary mode) then a continuous location dependent query (inmobile mode). In order to cope with the frequently changinglocation, query processor should have a monitoring functionthat detects the mobility of the user and then informs thequery processor to dynamically change the query evaluationmode.

Location aware applications use location sensing systemslike GPS to locate objects in mobile computing. These lo-cation sensing systems have some limitations e.g., GPS cannot be used indoors, Active badges are affected by sunlightand fluorescent light which interfere with infrared (for moredetails we refer readers to [7]). Thus, the query proces-sor should have the ability to detect other location sensingsystems and then toggle to the one that best meets queryevaluation needs.

Traditional query evaluation techniques generally depend onthe application. They are optimized to deliver a completeanswer and do not hold take into account the user prefer-

ences. However, these preferences are important in a con-strained environment. For example, in an ad hoc applica-tion, a user may wish to receive quickly the results of hisrequest even if they are incomplete. On the other hand, ina B2B environment, the user may wish to receive completeresults and as soon as possible.

6. CONCLUSIONMobile computing is the future vision of the world. In suchan environment, many challenges have arisen but generally,the proposed solutions only try to treat a particular prob-lem. For example, [13] presented a system framework for

query processing in ad hoc wireless networks which onlysupports simple queries, and does not take query optimiza-tion problems into consideration. As information will beaccessed from anywhere, at any time, and from any termi-nal, the need of an adaptable query processing, allowing amore effective evaluation, has appeared. Furthermore, thisquery processor should have a flexible architecture that canadapt to device capabilities and heterogeneity. In this arti-

cle we defined the main constraints that a query processorcan face in a mobile environment and presented the needsfor adaptability when deployed in a mobile constrained anddistributed environment.

7. ACKNOWLEDGEMENTThis work is done as part of the MOSAIQUES project sup-ported by FEDER and the region of Nord-Pas-de-Calais.

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