Developing Xquery Model for Distributed Query Processing in Mobile Networks

Abstract

The emergence and popularity of mobile computing environment, so get a variety of semi-structured data to follow some common XML model. The Extensible Markup Language (XML) model has recently gained huge popularity because of its ability to represent a wide variety of structured and semi-structured data. Several Query languages have been proposed for the XML data model, the most-widely known is XQuery. Traditional query processing to a database focused on structured data retrieval and structures to support them. In this paper we present a model and an algorithm for querying structured and semi -structured data for mobile computing environments based on the model of XQuery. We employ a variety of servers to handle different jobs. Buffer is maintained in the mobile node and the cache is stored in the query server, and mobile server. Priority is given to requests based on various parameters such as priority by the user, the required bandwidth, etc. parameters considered for performance measure of the effectiveness of the request, delivery ratio and average power consumption and the results show that the proposed algorithm works better than existing systems.

Full text

International Journal of Computer Applications (0975 – 8887)
Volume 25– No.4, July 2011
36
Developing Xquery Model for Distributed Query
Processing in Mobile Networks
T.P.Andamuthu
Professor
Maharaja Engg college for Women, Perundurai,
Tamilnadu, India
P.Balasubramanie
Professor
Kongu Engg College, Perundurai, Tamilnadu,
India
ABSTRACT
The emergence and popularity of mobile computing
environment, so get a variety of semi-structured data to follow
some common XML model. The Extensible Markup Language
(XML) model has recently gained huge popularity because of its
ability to represent a wide variety of structured and semi-
structured data. Several Query languages have been proposed
for the XML data model, the most-widely known is XQuery.
Traditional query processing to a database focused on structured
data retrieval and structures to support them. In this paper we
present a model and an algorithm for querying structured and
semi - structured data for mobile computing environments based
on the model of XQuery. We employ a variety of servers to
handle different jobs. Buffer is maintained in the mobile node
and the cache is stored in the query server, and mobile server.
Priority is given to requests based on various parameters such as
priority by the user, the required bandwidth, etc. parameters
considered for performance measure of the effectiveness of the
request, delivery ratio and average power consumption and the
results show that the proposed algorithm works better than
existing systems.
General Terms
Mobile Computing, Distributed Computing, Databases
Keywords
Query Processing, Mobile computing, cache, query efficiency
etc.
1. INTRODUCTION
Today, small notebooks, PDAs and smart phones are getting
smaller, portable solution possible to access digital information
in an easy manner. Mobile terminals communicate and interact
with other terminals via wireless networks such as Wi-Fi or
Bluetooth. They may spontaneously network with each other
through their proximity and perform proximity applications [1-
10]. These devices will become both sources and consumers of
data and can communicate with other devices to meet individual
or collective computing. In such a mobile environment the
special elements of the network is very dynamic and can be
extremely volatile. Traditional query processing techniques[1-
12], the global data and statistics collected according to the chart
and the histogram is no longer such a very mobile environment,
sufficient for many reasons, such as: limited resources, and
powerful servers, the data types of terminals ranging from PDAs
to communicate the type of energy to a higher bandwidth or
limited this may be the heterogeneity bandwidth to a wired
network with wireless network. In addition, the query
optimization and execution of queries in order to evaluate
effectively the account must take into account available
resources. The number of small devices such as PDAs, smart
phones, and sensors of different types of data moving rapidly
grows with them. access to computing and communications is
not only required a local, but also the way to the user from one
location to another.
The database is organized by a set of logically related data.
Traditional structured database using the relational model [1]
Relational database access or manipulate with a very high level
language called SQL. Statement of the kind referred to in the
request specifies the data obtained from the database. For query
processing, system software, database management systems
(DBMS), generates a number of implementation plans, choose
one with the lowest cost, and then executes it to retrieve the
requested data. The relational model has many advantages,
including simplicity, rigorous mathematical justification and
ease of programming and use. However, it is also a lot of
limitations, including poor modeling capabilities. To overcome
these drawbacks, new and powerful data model with semantics
is richer than the relational one, were introduced.
Among those models are the deductive [2], object-oriented [3]
and XML [4]. The XML (Extensible Markup Language) model
organizes data into documents. The XML data model has gained
huge popularity because of its ability to represent a wide variety
of unstructured and semi-structured data as well as its use in
integrating different data sources (traditional / relational
databases, data files, email messages, web pages …etc.) for
display on a variety of devices, including personal computers,
personal digital assistants (PDAs) and smart mobile phones.
Several Query languages have been proposed for the XML data
model, the most widely used one is XQuery [5]. XQuery is
based on XPath in which requested data is represented in some
order of requirements of the Xquery language. Recursive queries
are quite important in the context of XML databases.
Facilitating ubiquitous data access at any point of time to the
user is the thrust area of all such researches[22-25]. Critical
constraints and limitations of connectivity, unreliable
communication, low battery and memory, slow processing,
storage and moderate limited screen size presented as challenges
in data management for mobile computing, despite their
privileges and advantages. These factors require a condition of
adaptability of mobile devices to the network environment
available today. Location dependent data access by the user
when the devices are on the way somewhere is a prominent

International Journal of Computer Applications (0975 – 8887)
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37
feature of mobile computing applications. The distributed query
processing systems assume an 'always-on' situation in the
network, which can seriously compromise and amended by
intermittent connectivity. An error in the current system is the
lack of network connectivity. At times, the instantaneous
transmission of accumulated data available since the last upload,
through the connection may not be feasible. The possibility of
unwanted irrelevant information that has priority over vital
information can not be ruled out. Priority data for transmission
is essential for the mobile nodes for the beneficial use of
connectivity in the binding context of bandwidth-limited and
uneven. Node connectivity (server) to Manet, it is not forever.
The minimum requirement to stay connected to the node
network, it must have sufficient power to operate and the ability
to hear the broadcast of another node on the network at least.
Network nodes (servers) can operate in one of three modes of
power reduction that is designed, 'forward', 'receive' and
'standby'. In 'send' more energy is used for sending and receiving
messages. 'Receive' mode allows the processing of data and
receive messages while no processing, transmission or reception
of CPU happen in 'standby' mode.
The whole geographical area is divided into cells. These cells
are roughly circular in shape. At the center of each cell is a Base
Station (BS), also referred to as a server, which communicates
with the mobile devices in its cell area through the wireless
channel. Each BS serves an area and those areas are connected
via a wired network as referred to earlier [12]. When a mobile
device moves from one cell to another it begins communicating
with the new BS in the new cell. In a centralized mobile
database the database resides in the central server or BS. A
mobile user can get data from the server in two methods: pull-
based and push-based. In a pull-based method there are two
channels namely an uplink channel and a downlink channel,
which is also called the pull channel. A mobile device sends the
query to the server via the uplink channel and the query results
come to the device via the downlink channel. The downlink
channels are private to each mobile device. In a push-based
method the server broadcasts the data on a broadcast channel
and the mobile devices tune to that channel to retrieve their
necessary information [12-15]. In this case the server has to
periodically broadcast information to the clients. In a hybrid
model, the push-based method is extended by using an uplink
channel via which clients can send explicit requests [12-15].
In this paper, we propose the system with different servers for
different job processing like one server for processing location
based queries and another server for processing data queries.
The queries are prioritized based on different parameters like the
preference given by the user, size of the result to be transmitted,
bandwidth requirement, time to transmit and finally time of
request. The more important issue is the bandwidth is reused
based on the reusability concept. The nodes in the system are
distinguished as mobile node, mobile server and the query
server. The parameters considered for performance evaluation
are query efficiency, delivery ratio and average power
consumption.
2. BACKGROUND
A method for secure query processing in mobile databases has
been presented in [12]. Queries are sent to the server through
point-to-point channels by the mobile devices. Computation of
the superset of results of many such queries is made by the
mobile database server for broadcasting through a channel of
larger bandwidth. The mobile devices can retrieve the required
information by tuning in to that channel. Senders of the queries
can view the results of both their queries and those of others in
the same group. The undesirable aspect in this is, malicious
users have access to the results f the queries sent by some other
users. In (1) proposal of a method is made that denies any user
doing so even when the results of multiple queries are
broadcasted through the same channel and are accessible to all
users. In [13], the authors discussed the various challenges in
distributed processing of location dependent continuous queries
in a mobile environment by studying the different scenarios in
which both the querying unit and the object being queried are in
motion. The authors discussed a classification of different s
categories of location dependent queries and various solutions to
such complex queries. Caching techniques enable faster access
to data, minimizing the huge network traffic created because of
the processing of location dependent queries in a wireless
environment. The query results are cached for a further
reduction of the data transfer and reusability of the results. The
thrust area in this is the methods of processing and application
of such location dependent queries (2). The study presents and
classifies various methods of processing of different location
dependent queries and data management problems in evaluation
of such queries.
A continuous query processing system for intermittently
connected mobile networks that comprises of a delay-tolerant
continuous query processor distributed across the mobile hosts
has been proposed in [14]. In addition, a mechanism for
prioritizing query results has been designed that guarantees
enhanced accuracy and reduced delay. The conventional
continuous query processors send the results of continuous
queries instantaneously over the network, whereas the query
processor in [14] stores them in an output buffer.
Dorian C. Arnold Barton P. Miller [17] have proposed a scalable
failure recovery model for data aggregations in large scale tree-
based overlay networks (TBONs). They have formalized the
TBON model and its fundamental properties to prove that our
state compensation model properly preserves computational
semantics across TBON process failures.
Dragan Stojanovic et al. [18] address the problem of processing
continuous range queries over mobile objects, whose motion is
constrained by a spatial network. The query range represents the
user-selected area, the map window, the polygonal feature, or
the area specified by the distance from a reference point of
interest. In contrast to regular queries that are evaluated only
once, a continuous query remains active over a period of time. A
major challenge for this problem is how to provide efficient
processing of continuous queries with respect of CPU time, I/O
time and main memory utilization.
Wei-Shinn Ku et al. [19] proposed two novel algorithms for
processing k nearest neighbor queries and range queries on
spatial networks with privacy protection. The main idea is to
hide the exact mobile user location with a cloaked region. The

International Journal of Computer Applications (0975 – 8887)
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38
cloaked region covers the query requester and at least K − 1
other users based on the K-anonymity concept. The spatial
queries are executed based on both the cloaked region and the
underlying networks.
Giuseppe Amato et al. [20] proposed approach the WSN can be
programmed using a query language (MW-SQL), which offers
constructs, specialized for sensor networks. The query language
is offered by a JDBC driver which is encapsulated within the
OSGi framework.
3. DATA MODELS
3.1 Relational Model
The relational model [1] organizes data into relations stored as
two-dimensional tables. The column headers of a table represent
the attributes of the relation, whereas, the rows are the relation’s
tuples. A table in the relational model can also be viewed, as
shown in Figure 2, as a three-level tree where the root node
represents the table name, the 1st level nodes represent its rows
and the 2nd level nodes represent the individual values
contained within each one of these rows.
Relational databases are accessed using a very high-level
language called Structured Query Language (SQL). A statement
of this type, referred to as a query, specifies the data to be
retrieved from the database. To process a query, a software
system, the database management system (DBMS), generates a
number of execution plans, selects the one with the least cost
and then executes it. As a result the data that satisfy the query is
returned to the user. In relational databases, a given query plan
consists of a sequence of operations called the relational algebra
operations (select, project, join, transitive closure ...etc.). The
execution of these operations, especially join and transitive
closure, against very large collection of data is generally slow.
Many serial and parallel algorithms have been proposed to
efficiently execute these operations. Some of these algorithms
and their performance evaluation can be found in [6] – [12].
Fig.1: Relational Model Representation
3.2 XML Model
Many solutions for XML data management use relational
database technology as the underlying storage medium.
Supporting the ordered nature of the XML data in the relational
model context is an issue since order information is lost while
converting from XML to the relational data representation.
Many solutions for semi-structured data have been extended to
support XML data. These solutions tend not to support XQuery,
and more importantly, do not support order requirements of
XQuery expressions. Concurrently with these efforts to exploit
existing database technologies, native XML storage manager
systems have also been proposed. An advantage of such native
storage is that XML documents may be clustered in physical
XML document order, thus facilitating efficient
children/descendant access.
Fig.2: XML model Representation
3.3 Query Processing For Structured Data
Fig.3: Phases of Query Processing
Query processing deals with designing algorithms that analyze
queries and convert them into a series of data manipulation
operations. The main function of a query processor is to
transform high-level query (typically, in relational calculus) into
an equivalent lower-level query (typically, in some variation of
relational algebra). Fig. 3 shows the classic architecture for
query processing. This architecture can be used for any kind of
database system including centralized, distributed, or parallel
systems. The query processor receives a query as input,
translates and optimizes this query in several phases into an
executable query plan, and executes the plan in order to obtain
the results of the query. We use this architecture for mobile
computing environments [22-25].
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International Journal of Computer Applications (0975 – 8887)
Volume 25– No.4, July 2011
39
4. XQUERY MODEL BASED QUERY
PROCESSING
4.1 System Model
In the proposed system, we have mobile nodes, mobile servers
and centralized XQuery servers. The whole geographical area is
divided into cells and the cell shape is assumed to be hexagonal.
Mobile nodes are the nodes that belong to a particular cell of the
network. Mobile server is similar to the Base station of the
cellular network. Mobile users directly communicate with the
mobile servers. The XQuery servers are similar to the MSC in
the cellular network. Mobile servers directly communicate with
query servers. Buffer is maintained in the mobile node. Mobile
servers and query servers maintain the cache. The queries are
classified as location based queries and data queries. Location
based queries are the queries related to the location of the
mobile node, about the traffic or about the information in the
specified location to get the particular object etc. Data queries
are the queries to retrieve some information from the mobile
nodes.
Fig. 4 System Model
When the mobile nodes require a piece of information, the query
is sent to the mobile server which is in its same cell. The queries
at that instant of time are prioritized based on the user
specification. If some queries are given the same priority by the
users, then they are sorted according to the time of request. Even
then if some queries are equally prioritized, they are sorted
based on the estimated bandwidth requirement which depends
on the size of the result to be transmitted. Finally if any collision
occurs in prioritization random method is used. After the queries
are prioritized, queries are optimized and processed in the
mobile server if possible. The queries can be processed in the
mobile server if the required information is fully available in the
server. Otherwise the query is forwarded to the Xquery server
based on the type of the query. If the query is location based, it
is sent to location query server otherwise it is sent to the data
query server. The query is processed by the corresponding query
server and cached. The output is sent to the mobile server. The
mobile server also caches this information and sends the output
to the required mobile node. The importance of the cache here,
data will be ready if similar queries occur. This improves the
performance of the system to maximize the delivery rate. The
queries also need not be sent to the query server one at a time.
More number of queries can be sent at a time to the query server
by the mobile server by maintaining the buffering system. This
is again done based on the priority. If the query is highly
prioritized, the processing takes place immediately otherwise it
is buffered for a bit of time period. This process reduces the
power consumption as the number of times to transmit and
receive data will be reduced.
Our proposed approach supports different types of XQuery
order, namely document order and order imposed by the query
itself in a variety of ways. A key point in our solution is that
caching the queries and its updates. Caching popular queries and
reusing results of previously computed queries are one important
query optimization technique, especially in modern distributed
environments such as mobile and ubiquitous environments.
Based on the recent proliferation of XML data and the
emergence of the XQuery language, developing a system based
on a query caching for XQuery and rewriting strategies to
respond to the user entering based query caching XQueries,
whenever possible, instead of accessing remote XML data
sources provides effective information retrieval.
5. RESULTS AND DISCUSSIONS
This section presents experimental evaluation of our DQPC
architecture when XQuery model is used. In order to test our
protocol, The NS2 simulation software [10] is used. NS2 is a
general-purpose simulation tool that provides discrete event
simulation of user defined networks. The network nodes were
placed uniformly at random within a square of 1000 meters. We
varied the average speed of the mobiles from 10, 20, …, 50 in
order to study the impact of server mobility. The data broadcast
size is varied from 1000, 2000… 5000. In all the experiments,
we used the following evaluation criteria:
5.1. Simulation Parameters
Average Power Consumption The average power consumed by
clients and the average power consumed by servers are
calculated.
Query Efficiency The data pull section will rely on the
measurement of query efficiency. This is a measure of the
percentage of data queries that get served during an entire
simulation.
Delivery Ratio which is the ratio of results received by the
clients and the no. of queries sent.
1. mobile node sends a query to mobile server
2. mobile server categorize the query
3. Buffer the query based on the category
4. Is data (location based) buffer?
i. prioritize the queries in the buffer
ii. If (can be processed in the mobile server?) then
1. queries are processed
2. cache the results
3. broad cast the results
iii. else
1. queries are forwarded to the data (location) query
server
2. queries are processed
3. cache the results
4. sent to the mobile server
5. Mobile server broadcasts the results.

International Journal of Computer Applications (0975 – 8887)
Volume 25– No.4, July 2011
40
5.2 Simulation Results
0
0.5
1
1.5
2
2.5
3
3.5
4
0 1000 2000 3000 4000 5000 6000
Load
Avg. Power Consumption
FDQP
DQPC
Fig. 5 Load vs Average power consumption
In Fig 5, when the server load increases, the average power
consumption for server decreases as less time is spent for
transmitting. The results in Fig. 2 shows that the proposed
algorithm DQPC performs better than the FDQP (Fault-tolerant
distributed query processor) because of caching. It can be
observed that the performance increases as the load increases.
This is because of the caching property. As the number of
accesses increases, the cache hits occur then the performance
increases. So, the results are mostly dependent on the cache
performance.
0
10
20
30
40
50
60
70
80
90
0 1000 2000 3000 4000 5000 6000
Load
Query Efficiency
FDQP
DQPC
Fig. 6 Load vs Query Efficiency
Fig. 6 shows that the performance of FDQP and DQPC
increases as the system load or broadcast size increases and it
also shows that the proposed algorithm DQPC increases the
efficiency of the query processing when compared to the FDQP.
0
10
20
30
40
50
60
70
80
90
100
0 20 40 60 80 100 120
Number of Que ries
Delivery Ratio
FDQP
DQPC
Fig. 7 Number of Queries Vs Delivery Ratio
The delivery ratio of the client in FDQP is compared with
delivery ratio of the mobile server as the role of client and
mobile host server in FDQP is played by the mobile server in
DQPC. The results in Fig. 7 show that the delivery ratio is
increased with the proposed algorithm.
6.
CONCLUSIONS
X-Query based distributed query processing algorithm based on
caching technique is proposed in this paper. The different
servers are maintained for query processing based on the type of
the query. Cache is maintained at both mobile server and query
server. At the same time buffering technique is implemented at
the mobile server. The queries are prioritized to improve the
performance. The main advantage of the proposed algorithm is
cache technique. The advantage of using X-Query model is its
easy to implement and extend to mobile computing platforms
because of its interoperable and portable properties. The
simulation is carried out and compared with the fault-tolerant
distributed query processing algorithm and is shown the
performance of the system is improved.
7. ACKNOWLEDGMENTS
Our thanks to the experts who have contributed towards
development of this paper.
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