AN ARCHITECTURE FOR LOCATION-DEPENDENT
SEMANTIC CACHE MANAGEMENT
Heloise Manica , MAR Dantas
Department of Informatics and Statistics - Federal University of Santa Catarina (UFSC)
Campus Universitario Trindade, Caixa Postal 476
Florianópolis – SC, Brasil, C
EP 88.040-900
Murilo S. de Camargo
Department of Computer Science - University of Brasília (UnB)
Campus Universitário Darcy Ribeiro
Brasília – DF, Brasil, CEP 70.910-900
Keywords: Mobile Computing, Mobile Databases, Cache Management, Semantic Cache, Location-Dependent Systems.
Abstract: Location-Dependent Information Services is an em
erging class of application that allows new types of
queries such as location-dependent queries and continuous queries. In these systems, data caching plays an
important role in data management due to its ability to improve system performance and availability in case
of disconnection. In mobile environment, cache management requires more than traditional solutions. This
paper presents a new semantic cache scheme for location dependent systems based on spatial property. The
proposed architecture is called as Location Dependent Semantic Cache Management – LDSCM. In addition,
we examine location-dependent query processing issues and segment reorganization.
1 INTRODUCTION
Advances in mobile computing make possible the
development of new classes of services such as
Location-Dependent Information Services (LDIS).
These services emerged with a variety of promising
applications, such as local traffic report, hotel or
restaurant information and emergency services.
The access data, called Location Dependent Data
- L
DD, are related to their geographical position and
the queries that issue for them are named Location-
Dependent Queries - LDQ (Ren and Dunham, 2000).
A location-dependent query usually originates from
a moving client, and the location determines the
query results. “Find all restaurants within 10 miles
of my position” is an example of a LDQ. The
client’s geographical location is provided by a
location service or global positioning system.
Data management in an LDIS faces several
chal
lenges that have opened many new research
problems (Lee et al., 2002). Mobile computing
environments offer low-quality communication,
frequent network disconnections and limited local
resources. In addition, because of mobile user’s
movement, some tasks such as data cache
management are tough.
Data caching plays a key role in data
m
anagement in mobile computing since it helps to
save power consumed with server communication.
Besides, data caching improve data availability in
case of disconnection. The mobile users are still able
to work using the cached data when the network is
unreachable.
In LDIS, cached data become invalid not only as
a resu
lt of updates performed on data items in the
server but also when a mobile user changes its
location (Zheng et al., 2002). Traditional solutions
are not suitable for LDIS and additional spatial
properties such as data distance and movement must
be considered. In conventional caching schemes
such as tuple-based or page-based it is difficult to
explore spatial properties because the cached data
are not associated with any semantic meanings.
A better alternative for mobile computing
envi
ronment is the semantic caching (SC) model
(Dar et al, 1996; Lee et al., 1999; Ren and Dunham,
2000; Ren et al., 2003). The semantic cache stores in
sets, named semantic segments, results of queries
320
Manica H., Dantas M. and S. de Camargo M. (2005).
AN ARCHITECTURE FOR LOCATION-DEPENDENT SEMANTIC CACHE MANAGEMENT.
In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 320-325
DOI: 10.5220/0002524903200325
Copyright
c
 SciTePress

submitted to the server (Ren and Dunham, 2000).
Moreover, the SC maintains semantic descriptions
associated to the answers of queries organized in an
index structure. The semantic segments are disjoint
and each tuple in the cache is associated with exactly
one semantic segment. This model is ideal for
location-dependent applications since it makes cache
management more flexible. With semantic
information, strategies with higher cache hit ratio
can be developed.
In a previous work (Manica et al., 2004-b), we
propose a new semantic cache scheme for LDIS
based on spatial property. In this paper, we
improved our ideas, propose an architecture named
Location Dependent Semantic Cache Management
(LDSCM) and we better define our location-
dependent query processing issues and semantic
segments reorganization.
The remaining of this paper is organized as
follows. Section 2 present related previous work. In
section 3 we propose the semantic cache model and
the algorithms for query processing and cache
management issues. The design of our semantic
caching architecture is presented in section 4.
Finally, the section 5 concludes this article and
present future works.
2 RELATED WORK
Querying location dependent information in mobile
environment has been an important research area. In
(Lee et al., 2002) the authors discuss location
dependent information access in a mobile-pervasive
environment and present new research issues.
Zheng et al., (2002) introduce a new
performance criterion, called caching efficiency, and
propose a generic method for location-dependent
cache invalidation strategies. In addition, Zheng et
al. proposed two cache replacement policies:
probability area and probability area inverse
distance.
Xu et al., (2003) proposed three schemes to
handle the location-dependent cache invalidation
issue: bit vector with compression, grouped bit
vector with compression and implicit scope
information.
Another related area is the semantic cache
model. (Dar et al., 1996) were the first work to use
semantic distance function for replacement policy.
They utilize the Manhattan distance function to
calculate the distance of each semantic region from
the user’s current location. Their algorithm discard
regions according to the values computed.
(Ren and Dunham, 2000) extend the work of Dar
et al. to investigate ways in which semantic caching
can be used to manage location-dependent data.
Their work includes a formal model to represent
moving objects and propose strategies of applying
semantic caching to location dependent applications.
For cache replacement, Ren and Dunham (2000)
propose a semantic cache replacement policy called
FAR (Furthest Away Replacement).
Similar to ours, other works such as (Ren and
Dunham, 2000) uses the semantic caching model to
manage location-dependent data. However, our
cache management strategies are based on the
geographical relationship between the query and the
semantic segments. Our semantic cache model group
in the same semantic segment, results of queries
with related predicate and also spatial properties.
The spatial information is the minimum bounding
rectangle that represents the valid spatial scope of a
semantic segment.
3 LOCATION-DEPENDENT
SEMANTIC CACHING MODEL
Different query types require different indexing and
query-processing strategies. Initially, this model
supports window query (Gaede and Graefe, 1998).
Given a rectangle d-dimensional R ⊆ E
d
, a
window query searches all objects whose geographic
position is inserted in the rectangle. The window
extension is determinate trough the mobile client
current location and the distance expressed in the
query predicate. The supported operator is selection
on single relations.
The following definition describes the location
dependent query supported by cache.
Definition 1 (Location Dependent Query)
Given a database D = {R
i
}, a location dependent
query Q is a tuple (Q
R
, Q
P
, Q
J
, Q
L
, Q
C
) where Q
R
∈ D
represents the relation issued, Q
P
a location
predicate, Q
J
the window query, Q
L
the mobile client
current location and Q
C
represents the query results.
From now on, we also refer to location
dependent query as the single word query.
3.1 Model Organization
The following definition describes the semantic
segment.
Definition 2 (Semantic segment) Given a
relation R and its attributes set A
1
, A
2
…A
n
, a
semantic segment S on relation R is a tuple (S
ID
, S
R
,
S
P
, S
A
, S
C
, S
TS
, S
G
) where S
ID
stands for the segment
identifier, S
R
the relation issued by the query, S
P
the
select condition, S
A
the valid spatial scope of the
AN ARCHITECTURE FOR LOCATION-DEPENDENT SEMANTIC CACHE MANAGEMENT
321

segment, S
C
the first page address of the segment
content, S
TS
a timestamp and S
G
the segment group.
To maintain the spatial information S
A
, we use
the minimum bounding rectangle (MBR) (Ix = [x
1
,
x
2
] and Iy = [y
1
, y
2
]), that represents the geographic
area of all data in the segment. MBR is a simple and
efficient method to store geographic scopes. The
semantic cache index is illustrated through the
following example.
Example 1: Consider an object-relational
database in the server with two relations: Restaurant
(
id, name, type, geometry), Hotel (id, name, price,
geometry). The mobile unite position is represented
by MU
P
= (x, y) and the following queries Q
1
and Q
2
are issued on time T
1
and T
2
respectively:
Q
1
UM
P
(10, 20): “Give me all hotels within 5
miles with price lower than U$ 100”.
Q
2
UM
P
(20, -20): “Give me all Chinese
restaurants within 10 miles”.
Suppose that the client cache can’t contribute
with the answer. Thus, these queries are sent to the
server and the results are stored in cache. The
semantic cache index is shown as Table 1. Next
section addresses the query processing over
semantic cache scheme.
3.2 Semantic Query Processing
To perform a query, it is checked whether the query
can be answered by the cache. If the query result can
be totally obtained from the cache content, there is
no communication with the server.
When the query can only be partially answered,
there are two disjoint portions: one that can be
answered in cache (probe query) and another that
requires data to be fetched from the server
(remainder query). The procedure of query partition
is named query trimming (Ren et al., 2003). We
define the probe and reminder query as follows.
Definition 3 (Probe Query) Given a query Q on
a relation R, S={S
i
} denotes the set of semantic
segments in the client cache that contributes with the
query answer, where S
iR
=Q
R
, S
iA
∩ Q
j
<>0 and
Q
P
∩ S
P
<> ∅ . The probe query is the union of all
partial results obtained from each segment in S.
Definition 4 (Reminder Query) Given a query
Q on a relation R. The remainder query (RQ)
retrieves from the server the portion of Q not found
in cache, that is, RQ = Q – PQ.
According to the definitions above, PQ ∧ RQ =
false and PQ ∨ RQ = Q.
That is to say, the probe and the remainder query
do not overlap and trough the union of the answers
to these two queries we obtain the complete answer.
Our query processing model involves two steps:
1) to select the semantic segments candidate set; 2)
to process the query against each candidate segment
and the database in the server when is necessary. We
present the algorithm 1, which selects the set of
semantic segments (CjSC) that are candidate to
answer the probe query.
Algorithm CjSC (Q, SC)
Input: Query Q, Semantic Cache SC
Output: Semantic segments candidates set (CjSC)
{ Cj Å null
for each relation R
i
in the index structure do {
if (R
i
= Q
R
) then
Cj Å segments S
i
…S
n
}
for each segment in Cj do {
if intersection (S
Ai
, Q
J
) = null then
Cj Å Cj - S
i
}
for each segment in Cj do {
if (Q
P
⇒ S
P
) or (Q
P
∧ S
P
) then
CjSC Å S
i
} }
Algorithm 1: Semantic segments candidate set selection
Given a query Q and a semantic cache SC, firstly
the algorithm selects the semantic segments that
have the same relation of Q, that is, S
R
= Q
R
.
Secondly, for each selected segment, its geographic
area is compared with the window query. If the
semantic segment that doesn’t have any relationship
with the window query it is eliminated from the set.
Lastly, our algorithm analyzes the predicate
similarity. At the end, we have the semantic
segments that probably contribute to the answer.
We illustrate this procedure with the example2.
Example 2: Consider that Q is issued by UM
P
(15,45): “Give me all hotels within 5 miles and
diary price lower than U$ 100” (figure 1). According
to the algorithm 1, Cj = {S
1
, S
3
, S
6
, S
7
}. The
segment S
6
is eliminated form Cj because its
intersection with Q
j
is null. Finally, the segments
from Cj whose predicate satisfies the query predicate
are added in CjSC set.
To avoid false segments selection, it is possible
to use others geometric forms, such as circles or
complex polygons. However, such representations
consume a large portion of space and they introduce
additional complexity to determine intersection
areas.
Table 1: Exam
p
le of Semantic Cache Index
S
ID
S
R
S
P
S
A
S
C
S
s
S
G
S
1
Hotel price<100 [(5,15), (15,25)] 4 T
1
1
S
2
Restaurant type = “Chinese” [(10,-30), (30,-10)] 8 T
2
1
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322

Figure 1: Example 2, semantic segments candidate
Next, we present the algorithm 2 which processes
the query against the semantic segments in cache
and the database if necessary.
Algorithm QueryProcess (Q, CjSC )
Input: Q e CjSC
Output: Q
R
{ For each S
i
in CjSC do {
I
i
Å intersection (S
Ai
, Q
J
)
If Q
P
∩ S
iP
= Q
iP
then { //** Q
P
^ ¬ S
iP
=0
Execute Q
i
in S
i
and area I
i
PQ
R
Å PQ
R
∪ Q
iR
X Å X + I
i
ReorgSeg (Q, I
i
, S
i
) }
Else (Q
P
∩ S
iP
≠ Q
iP
) {
AQS
iP
Å (Q
P
^ ¬ S
iP
) and area I
i
Send to server AQS
i
PQ
R
Å PQ
R
∪ AQS
iR
Execute Q
i
in S
i
and area I
i
PQ
R
Å PQ
R
∪ Q
iR
X Å X + I
i
ReorgSeg (Q, I
i
, S
i
) } }
If X <> Q
J
then send to server RQ = Q ∧ ¬X
Q
R
= RQ
R
∪ PQ
R
Create a new semantic for Q
R
}
Algorithm 2: Query processing in segments candidate set
For each segment in CjSC the following steps are
applied. First, is defined the intersection area
between the query window (Q
J
) and the segment
geographic area (S
iA
). Following, the algorithm
verifies the relationship between the segment (S
iP
)
and the query predicate (Q
P
). If the segment S
i
satisfies the query, it is not required data from the
server. The query is executed over S
i
and its results
contribute with the probe query.
When a semantic segment S
i
answer partially the
query, a partial sub-query named amending query is
sent to the server. This amending query issues only
the predicate that is not in S
i
.
The intersection area I
i
that it has already
researched in cache is stored in the vector X. Last of
all, if X <> Q
J
, the algorithm executes the reminder
query in the server. The reminder query, represented
by Q
J
∧ ¬X, returns all tuple that are inside the
window query and that are not in X. We illustrate
the algorithm 2 with the following example.
Example 3: With the query described in
Example 2, suppose that S
1
and S
3
contribute with
the query answer. Assume that the segment S
3
stores
hotels with price lower than U$ 50 and the segment
S
1
stores hotels with any price. In this case, S
3P
satisfies partially Q
P
and S
1P
satisfies totally Q
P
.
Note that for the segment S
3
, all its contents
contribute to the result of Q and the remaining data
(hotel with price between U$ 50 and U$100) are
requested to the server (amending query AQS
3
).
Otherwise, S
1
has the complete predicate to answer
Q and the amending query is not required.
We use the vector X that stores the rectangle of
the areas already searched in cache. In Example 3,
the reminder query is executed as follows:
“SELECT * FROM Hotel WHERE Hotel.price <
100 AND ((Hotel.geometry IN Q
J
) AND
(Hotel.geometry NOT IN X))”.
3.3 Semantic Segment Reorganization
The previous sections described our SC model as
well as the query processing. In this section, we deal
with the scenario after query processing and
semantic segment reorganization.
To avoid redundant data, we cannot keep both S
and Q in the cache. When a query is executed, if it is
completely answered by the server, the complete
answer is stored in a new segment. However, when a
portion of the query answer is already in cache, only
the data brought into the cache as the result of the
remainder query should be stored in a new semantic
segment. Therefore, the semantic segments can be
modified dynamically based on the queries that are
executed at the client.
The way that semantic segments are formed has
a significant effect on the performance of the
semantic cache (Jónsson, 1998). In addiction, this
model considers the geographic relationship between
Q and S to reorganize the semantic segments. The
algorithm 3 executes the segment reorganization.
Procedure ReorgSeg (Q, I
i
, S
i
)
Input: Q, I
i
and S
i
{ If I
i
< > S
Ai
then {
S
Ai
Å S
Ai
- I
i
If S
Ai
< > rectangle form then {
Adjust S
iA
with a rectangle representation } }
S
i
Å S
i
– ( (S
i
∩ Q) in I
i
^ (S
i
^ ¬ Q) in I
i
)
If S
i
^ ¬ Q < > null then {
Create a new segment S
i
’
S
i
’ Å (S
i
^ ¬ Q) in I
i
} }
Algorithm 3: Semantic segment reorganization procedure
S
6
S
1
S
3
Q
J
S
7
S
2
S
5
S
4
Legend:
S
R
= Hotel
S
R
<> Hotel
AN ARCHITECTURE FOR LOCATION-DEPENDENT SEMANTIC CACHE MANAGEMENT
323

Given a query Q, a semantic segment S
i
and the
intersection area between Q and S
i
named I
i
, the
segment reorganization is given as follows. Firstly,
the procedure verifies if I
i
is different from S
Ai
.
When a window query Q
j
covers totally S
A
(figure 1
- segment S
1
), it is not necessary to actualize S
Ai
because the intersection area I
i
is the same of S
i
.
On the other hand, when S
Ai
∩ Q
j
is different
from S
Ai
(figure 1 - segment S
3
), it is necessary to
reduce from S
Ai
the intersection area I
i
. If the
remaining geographic area has a complex form, it
must be adjusted as a rectangle shape.
After geographic area verification, the semantic
segment content should be updated. The data used to
answer Q must be removed from S, and if it remains
data, these remaining data should form a new
semantic segment S
i
’. The complete result of Q
always will generate a new segment. We illustrate
this procedure with the following example.
Example 4: Consider the query “Give me all
hotels within 5 miles with price higher than U$
100”. Suppose that the segment S
3
stores hotels with
diary price higher than U$ 50. In this case, S
3P
satisfies Q
P
and also S
3A
intersects partially with Q
J
.
After query execution in S
3
, this segment must be
reorganized (Figure 2).
Note that S
A3
∩ Q
J
is different from S
A3
, then S
A3
Å S
A3
- I
3
. The resulting area has a complex form,
and then it is adjusted as a rectangle shape. The data
used to answer Q (S
3
∩ Q) must be removed from S
3
(that is, Hotel.price > 100 in I
3
). The remaining data
S
3
^ ¬ Q (that is, Hotel.price > 50 and Hotel.price
< 100 in I
3
) is not null, then a new segment S
3
’ is
created with these remaining content.
Figure 2: An example of Segment Reorganization
4 LOCATION DEPENDENT
CACHE ARCHITECTURE
In this section we propose a Location Dependent
Semantic Cache Management – LDSCM (figure 3),
an architecture that support LDQ and provides
semantic cache management based on spatial
property. In the server, placed at the fixed network,
an object-relational database system stores and
manages the LDD. The mobile clients communicate
to the server through a wireless link and are
responsible for storing and managing its semantic
cache. Next section details the LDSCM components.
The component LDQ Query Generator generates
location dependent queries according to the database
scheme in the server. This module is responsible for:
(1) to elaborate a query; (2) to bind the user current
location and to send the query to the component
LDQ Query Processor; (3) to receive the complete
answer from LDQ Query Processor and to send it to
the application.
Initially, an application won't be represented for
user interface because the main objective of this
prototype is to test the proposed solutions. Once
proved the benefits of this new model, this will be
able to be implemented according to the
characteristics of different mobile devices, such as
notebook, palm, PDA and so on.
The component LDQ Query Processor is
responsible for the following tasks: (1) to determine
and execute the probe query, amending query and
reminder query, according to relationship with the
semantic segments in cache; and (2) to combine the
results obtained in cache with the results shipped
from the server and return the complete answer to
the component LDQ Query Generator. To
accomplish these tasks, the LDQ Query Processor is
composed by the following modules: Segments
Candidates Selector and Query Divisor and
Executor. The module Segments Candidates
Selector is responsible for determining the set
(CjSC) of semantic segments that are candidates to
answer the query Q. This process is executed using
the algorithm 1, as described in section 3.
Once defined the CjSC, the Query Divisor and
Executor component defines and execute the probe
query for each segment in CjSC as shown in
algorithm 2. This module also sends to the server the
remaining and amending queries when necessary.
The server receives queries from the client,
processes them, and sends the results back.
The complete probe query is composed by one or
more sub-queries. The reminder query result is
combined with the probe query to compose the
complete answer.
The portion of the answer that is not in cache
(reminder query) must be sent to the component
Cache Structures Manager. This component
reorganizes the semantic segments, as described in
algorithm 3.
The Cache Structures Manager is also
responsible for semantic caching replacement. If
necessary, the replacement is performed using the
ASCR policy (Manica et al., 2004).
Q
New
S
S
3P
∩ Q
P
in I
i
S
3P
-(S
3P
∩Q
P
) in I
i
I
3
S
3
S
3
S
3
- I
3
with complex
shape adjust
S
3
’
ICEIS 2005 - DATABASES AND INFORMATION SYSTEMS INTEGRATION
324

5 CONCLUSIONS AND FUTURE
WORK
Semantic caching is a new client caching
architecture, which focuses on reducing network
traffic and improving data availability in case of
disconnection.
We proposed a new semantic caching model for
location dependent information systems. The
characteristics of the SC architecture, SC query
processing strategy and practical issues of client
caching management were described. One of the
main contributions of this work is the use of
geographic information on cache management.
Here we describe the design of our prototype, the
next step is to study its performance and compare
with other proposals.
Future studies also will explore semantic cache
management issues for complex location-dependent
queries.
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Wired
Network
Wireless Link
SGBD
Objeto-
Relacional
Semantic Cache
DB
Cache Structures Manager
LDQ Query Processor
LDQ Query Generator
Q
i
Q
Ri
AQ
i
/ RQ
AQ
iR
/ RQ
R
RQ
Query
Result
Database
Scheme
Segments Candidates Selector
Query Divisor and Executor
CjSC
Mobile Client
Figure 3: LDSCM Architecture
Server
AN ARCHITECTURE FOR LOCATION-DEPENDENT SEMANTIC CACHE MANAGEMENT
325