INTEGRATING E-LEARNING OBJECTS IN A P2P SYSTEM
Gabriel André D. Dubois Brito
Military Institute of Engineering - IME/RJ - Engineering System Department - Rio de Janeiro - Brazil
Ana Maria de C. Moura
Military Institute of Engineering - IME/RJ - Engineering System Department - Rio de Janeiro - Brazil
Keywords: Peer-to-peer technology, Distributed and Parallel Applications, Data Semantics, Information Integration.
Abstract: ROSA is an e-learning system, which enables the creation, storage, reuse and management of Learning
Objects (LOs). LO is a collection of reusable material used to support learning, education, or training.
However, since ROSA is still a centralized system, it does not provide yet a complete integration of LOs
created in local ROSAs of other institutions. This paper presents the evolution of ROSA into a peer-to-peer
(P2P) system - the ROSA - P2P - and describes the integration process of LOs in this environment. It
provides the required interoperability to execute queries throughout all ROSA - P2P peers, taking into
account a strategic data integration system that includes queries rewriting based on their semantic meanings.
Controlled vocabularies are also used to support the query rewriting process and the identification of
relevant peers that are able to answer queries on a specific knowledge domain.
1 INTRODUCTION
ROSA (Repository of Objects with Semantic
Access) (Porto et al., 2004) is an e-learning
centralized system used by academic professionals
in educational area. It has been conceived to support
the design phase of e-learning courses, where tutors
want to share and search for course material.
However, in order to confer a real role of inter-
institutional cooperative environment on ROSA, it is
important that contents can be stored in different
institutions, before being interchanged and
integrated. This would be essential to provide global
answers to queries submitted by users through local
ROSAs.
Advances in distributing computing, fostered by
the real need to exchange information on the Web,
led to the development of standard interconnection
specifications to support semantic data
interoperability and integration. In this context,
metadata, ontologies and P2P technologies raise as
important research issues to support semantic
heterogeneity integration worldwide. The former
provides for data description concerning different
schemas, including mappings, associations, source
locations, etc., furnishing essential information for
data integration. Ontologies are used to represent the
semantics of a knowledge domain, and in
conjunction with metadata and controlled
vocabularies, are essential to ensure the correct
query interpretation. Additionally, they are
responsible for providing systems interchange,
supplying them with more refining queries, and
enabling more relevant and precise answers. P2P is a
recent technology that aims to harness Internet-
connected resources at a global scale, and can be
self-organizing, ad-hoc and decentralized.
The purpose of this paper is to describe the
ROSA transformation process into a P2P
environment, generating the ROSA - P2P system
version, and to develop a strategy to integrate e-
learning objects in this system. In this context, a P2P
architecture based on super-peers was developed
(Brito, 2005), including specific strategies to
provide: peers connection/disconnection into/from
the P2P network, such as super-peers grouping based
on knowledge domain and location; super-peers
definition and election; peers balancing and
redistribution in the system; and some fault tolerance
issues. Another great contribution of this paper
concerns the e-learning objects integration strategy
214
André D. Dubois Brito G. and Maria de C. Moura A. (2006).
INTEGRATING E-LEARNING OBJECTS IN A P2P SYSTEM.
In Proceedings of WEBIST 2006 - Second International Conference on Web Information Systems and Technologies - Internet Technology / Web
Interface and Applications, pages 214-221
DOI: 10.5220/0001244502140221
Copyright
c
SciTePress
defined for ROSA - P2P, which is completely
described. Each query is only broadcasted to the
relevant peers in the system, which is rewritten
according to its domain semantics and executed
based on ROSA algebra (Coutinho and Porto, 2004).
Partial results returned from individual ROSA peers
are then sent to the requested peer, which is
responsible for the objects integration and
presentation to the user.
The rest of this paper is organized as follows.
Section 2 presents a brief description of ROSA
system, with its main objectives and functionalities.
Section 3 introduces P2P systems, focusing on the
main types of architectures, generally used to
implement these systems. Section 4 describes the
ROSA - P2P system, giving emphasis to its
architecture and strategies adopted for peers
configuration and e-learning objects integration in
this environment. Next, section 5, presents some
related work, and finally, section 6 concludes the
paper with additional comments and future work.
2 ROSA SYSTEM
ROSA main purpose is storing e-learning objects
(LOs), and exploring their access according to the
context in which they have been created. A LO is
identified by a set of metadata descriptors
established by an international metadata standard,
such as LOM
1
(Learning Object Metadata). These
metadata are organized into a hierarchy, providing
information about identifier, title, keywords, idiom,
version, aggregation level, etc. Indeed, LOs
represent instructional contents, whose contexts are
determined by semantic relationships between them.
These relationships are expressed through a
conceptual map, according to a well-defined model
(Porto et al., 2004).
A conceptual map is represented by a directed
graph where nodes correspond to LOs, identified by
their names, and arcs refer to relationships between
them, such as RDF
2
(Resource Description
Framework) predicates. ROSA also provides an
algebra and a query language, the ROSAQL (Porto
et al., 2004), so that semantic queries such as “which
course material does an OO Database topic
comprehend? and “which subjects are basis for
teaching Query Optimization?” are supported by
ROSA system, taking into account the predicate
semantics. In these examples comprehend and basis
1
http://ltsc.ieee.org
2
http://www.w3.org/RDF
for are part of a pre-defined predicate set that relates
different LOs. These can be of two types: logical and
physical LOs. A logical LO represents a collection
of LOs, which may contain several physical LOs;
and a physical LO corresponds to a stored LO, such
as files (.jpg, .doc, .ppt, etc.). Questions related to
synonyms, specific/generic, and associated terms are
supported by a domain thesaurus that helps during
query processing.
3 P2P SYSTEMS
P2P systems are characterized by the sharing process
of computing resources and services through a direct
and decentralized communication among systems
(Ooi et al., 2003). They can be classified according
to 3 basic types of architectures (Brito and Moura,
2005): partially centralized: contains a central
server responsible for the search mechanism and
infra-structure maintenance, leaving to the
participant peers the task of sharing resources and
services in a distributed way; decentralized: does
not have a central peer, and the search mechanism
and infra-structure maintenance, as well as services
and information contents, are distributed throughout
the network, in each participant peer; and super-
peer: is composed of a set of inter-linked peers with
higher computing capacity, named super-peers.
These are responsible for the management and
sharing of resources, where each super-peer has
other peers linked to it. Due to its characteristics,
this architecture raises as the most adequate for
developing and maintaining P2P systems, since it
provides, besides other advantages: time reduction
for research; fault tolerance; super-peers
management; scalability; and a reasonable accepting
confidence level (Brito, 2005).
Although it does not exist yet a consensus
concerning a well-defined topology for data
integration architectures in P2P systems, we adopted
a data integration strategy exclusively dependent on
the system objectives, architecture, functioning and
specific characteristics according to ROSA - P2P
system proposal. This strategy is very important in
P2P data integration systems, so that data can be
stored, filtered, accessed and integrated in an
optimized way. However, to provide more flexibility
in the schema mapping process, this strategy should
incorporate a semantic connotation. This is
accomplished using metadata, domain ontologies
and controlled vocabularies in order to improve and
facilitate the semantic interpretation and integration
of objects stored throughout the peers network.
INTEGRATING E-LEARNING OBJECTS IN A P2P SYSTEM
215
Due to the dynamic nature of the e-learning
environment, P2P technology raises as one of the
most appropriate infrastructure for developing this
kind of system, since it encourages the creation of
educational communities in an easy and cheap way;
allows information to be shared and organized by
didactic contents; increases the volume and quality
of instructional resources; offers the sensation of
data readiness at all moment to the user, still making
it possible efficient search, since queries can be
processed in parallel (Nguyen and Sanchez, 2004).
4 ROSA - P2P SYSTEM
ROSA - P2P aims at integrating e-learning objects in
a P2P environment, where users will be able to
submit queries using either a portal (Toledo, 2002),
named ROSA portal, either a ROSA - P2P peer.
Hence, when a query is submitted by a user through
a super-peer, the latter will verify if it is able to
answer the query. In affirmative case, it will rewrite
the query to itself, storing the result in cache. Then it
will resend the original query to its own peers and to
other relevant super-peers, activating a clock that
will control the time a super-peer will wait for
having results. Nevertheless, when all the results
will have been returned, or the corresponding
waiting time will have elapsed, all results stored in
cache will be integrated and the final query result
will be returned to the user.
The system proposal will be presented according
to its functionalities in the following sections.
4.1 Internal Architecture
Figure 1 illustrates the internal system architecture,
showing its modules and components, as following:
• Interoperability Module: is composed of the
interoperator component (P2P), which presents
the necessary characteristics and functionalities
to create and maintain a P2P network, such as
connection establishment, routing indices
maintenance, super-peers election and network
balance;
• Query Processing Module: is composed of
the user interface component, which is
responsible for providing a more friendly
communication environment between users; and
the query processing component, which adopts a
strategy classified in two phases, as presented in
section 4.3.2;
• Data Management Module: consists of two
components: controlled vocabularies, which
support semantic interpretation during query
execution. Indeed, it facilitates peers
information interchange, providing for more
precise searches and most relevant results; and
data cache/integrator, responsible for temporally
storing partial query results. Once received the
partial results from the relevant peers and/or
super-peers, the integration process is started,
after which the final query result is returned to
the user.
Figure 1: ROSA - P2P architecture.
4.2 P2P Environment
ROSA - P2P system has been developed according
to the characteristics described in section 3, and it is
based on a super-peer architecture.
Even though ROSA portal refers to super-peers,
it does not take part in the P2P architecture as a
whole. It is situated in a layer above, and it is used as
a starting point for users to submit their queries
through the Web, in case they do not have ROSA -
P2P at their disposal, as well as an exit point to
receive their query results. However, its hosting
machine is used to store some important services
provided by the system, such as the Directory
Service (DS), which is responsible for making
available the list of existing super-peers to the new
peers that want to connect to the system for the first
time; and the Controlled Vocabulary Delivery
Service (CVDS), responsible for storing all
controlled vocabularies (global, local and of
keywords) in this machine, external to the P2P
Interoperator (P2P)
ROSA - P2P Super-P eer
ROSA - P2P Supe r-Pe er
Data Cache/
Integrator
ROSA P orta l
Controlled
Vocabularies
User
Interface
Query Processor
ROSA - P2P
Pe e r
ROSA - P2P
Pe e r
ROSA - P2P Super-Pee r
Interoperator (P2P)
ROSA - P2P Super-P eer ROSA - P2P Super-P eer
ROSA - P2P Supe r-Pe er
Data Cache/
Integrator
ROSA P orta l
Controlled
Vocabularies
User
Interface
User
Interface
Query Processor
ROSA - P2P
Pe e r
ROSA - P2P
Pe e r
ROSA - P2P Super-Pee r
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environment. Hence, when a peer connects to the
system for the first time, it receives these
vocabularies through the network. These services
will be better described in sections 4.2.1 and 4.3.1
respectively.
4.2.1 Main Strategies
This system adopts a strategy based on peers
grouping, similar to the one defined in Edutella
system (Nejdl et al., 2003), referenced as peers
aggregation. However, in this work we used a more
comprehensive strategy, where this first idea has
been extended. It consists in grouping these
aggregations, called here super-peers grouping, as
shown in Figure 2. Aggregations are created
according to two important features: subject and
geographical localization, whereas groupings are
classified according to the subject dealt by the
corresponding aggregations. This strategy ensures
that peers with similar characteristics stay close to
each other, facilitating its localization and
optimizing query processing (Brito, 2005).
Figure 2: Aggregations and groupings in ROSA - P2P,
where IME, UFF, UFRJ, PUC are universities in Rio de
Janeiro.
There are two different ways to connect a peer to
the system: i) when the peer connects to the system
for the first time. In this case it sends a query to the
DS, requesting for a list of available super-peers.
Then it checks, among the existing super-peers,
those that have the same subject and localization
similar to its own (it is worthwhile mentioning that
this information is provided whenever a ROSA peer
is installed in the system). Once this information is
obtained, it sends to the respective super-peer a
connection request, which validates it, verifying if
the new peer can indeed take part in the P2P
network. This validation attests if ROSA - P2P
system is present in the system. Once the connection
is established with the super-peer, the latter has to
provide information about its metadata, such as:
machines’ name, IP address, subject, origin country,
if it wants to be a super-peer in the future, etc; ii)
when a peer wants to be reconnected to the system.
In this case, the peer has already a reference to its
super-peer, and hence it can automatically connect to
the system. In both cases, once connected, the peer is
already able to share resources and submit queries.
In order to be a super-peer, a peer must provide
some important physical characteristics to ensure a
good system performance (Zhu et al., 2003). In
ROSA - P2P, it was considered that every academic
institution would be automatically a super-peer.
Otherwise, some relevant information concerning
physical characteristics of that peer must be
provided by the user at the moment he/she installs
the system.
The number of existing super-peers is dynamic,
defined according to the maximum quantity of peers
that a super-peer can support. This quantity is
determined in function of the result time evaluation
of queries submitted to similar hardware machines,
situated in different locations (Brito and Moura,
2005). So, the ideal quantity of super-peers in the
system will be indirectly balanced.
Election of super-peers occurs only among
aggregations and groupings (instead of considering
all peers in the system), i.e., a peer can only be a
super-peer within its own aggregation or grouping.
System balancing happens whenever a super-
peer has more peers than another of the same
grouping. In this situation, there will be peers
redistribution among not balanced supper-peers of
each grouping, in order to ensure better performance
to queries results.
According to Nejdl. (Nejdl et al., 2002), the use
of super-peers indices minimizes significantly query
redistribution time among relevant peers, i.e., those
that are able to answer a specific query. Thus, the
system adopts a strategy based on routing indices,
such as in Edutella system, using two data structures,
named routing tables. The first concerns
communication between a super-peer and its
respective peers (SP/P); and the other provides
information of a super-peer and its super-peers
(SP/SP). This information focuses on data and
metadata of peers, such as: subject domain, location,
peer status (online/offline) and physical
characteristics, which are provided by each peer and
super-peer. This information is used to optimize the
Peers deal with
the sub ject
Chemistry and
are located at the
same country
Aggregation 1
PUC
2
1
1 3
2
Aggregation 2
Super-peer
Super-
peer
IME
2
1
Super-peer
Grouping 1
Super-peers deal with the sub ject Computer
Science,
independently of its geographical
localization
Super-
peer
1
2
UFRJ
UFF
Peers deal with
the sub ject
Psychology and
are located at
the same
country
Peers deal with
the sub ject
Chemistry and
are located at the
same country
Aggregation 1
PUCPUC
22
11
11 33
22
Aggregation 2
Super-peer
Super-
peer
IMEIME
22
11
Super-peer
Grouping 1
Super-peers deal with the sub ject Computer
Science,
independently of its geographical
localization
Super-
peer
11
22
UFRJUFRJ
UFFUFF
Peers deal with
the sub ject
Psychology and
are located at
the same
country
INTEGRATING E-LEARNING OBJECTS IN A P2P SYSTEM
217
query redistribution process among relevant peers.
Due to ROSA - P2P dynamic characteristics, these
tables must always be kept updated neither to
prejudice system performance nor its reliability. This
process is accomplished by triggers, which are able
to detect all modifications occurrences and update
them in the DS and routing tables.
The system also provides fault tolerance
mechanisms. These procedures increase system
reliability, since they are always available to solve
possible faults, avoiding it becomes inoperative.
4.3 Data Integration System
Taking into account the architecture, functionalities
and characteristics of ROSA - P2P system, it was
possible to define a valid architecture for data
integration. Each peer has its own data integration
component, including an entry query point, which is
also used for integrating results. A friendly interface
has been provided to interact with the user, allowing
him to submit queries in an easy way, where results
are exhibited clearly (Brito, 2005). Data integration
is supported by controlled vocabularies, responsible
for providing some semantic value to data. They
help finding data and relevant peers, rewriting
queries, and solving semantic conflicts during all the
integration process.
Data integration is still a great challenge, since it
depends directly on the way the semantics of a
concept is defined in a peer. This information is
essential to build a global integrated view, yet a
complex task to manage. Special attention has been
given to optimization aspects, in order to provide
simplicity, performance and reliability, as described
next.
4.3.1 Controlled Vocabularies
As already mentioned, ROSA - P2P system uses
controlled vocabularies to support data integration.
These structures raise as powerful tools to facilitate
semantic interpretation and information retrieval,
whereas providing systems interoperation and
enabling more refining searches, restricted only to
relevant information. In fact, they become essential
to: correctly locate relevant peers to answer a query;
help peers in the query rewriting process; solve
semantic conflicts; suggest options and associated
paths related to the corresponding search, helping
the user to reach his/her objectives; and in the
automation of tasks that require reasoning. In order
to reach this objective, the system uses three
different vocabularies:
• Global controlled vocabulary: is used by all
peers in the system. It is composed of a
synonymous vocabulary according to existing
predicates in ROSA system and some specific
properties borrowed from the thesaurus
approach, added of some LOs predicate
properties, such as transitivity and symmetry.
This vocabulary is very important in the
navigation rewriting operation (section 4.3.2),
enabling the query to be rewritten taking into
account all relevant data, independently of the
semantic used by each peer to describe a
predicate;
• Local controlled vocabulary: specifies
vocabularies according to each existing subject
domain referred to in the system. Thus, the
system will have as many local vocabularies as
the number of subjects treated in the system.
Differently from the global vocabulary, only
peers concerned with a specific domain will be
supplied of a corresponding local vocabulary.
This vocabulary is based on a thesaurus
structure, composed of equivalent, generic,
specialized and associated terms. It is also very
important to rewrite the selection operation
(section 4.3.2), since it allows a query to be
rewritten based on all its relevant concepts,
independently of the semantic used by each peer
to describe a LO.
• Keywords controlled vocabulary: consists
of a vocabulary associated to each existing
subject domain in the system. It makes it
possible to detect a query subject in running
time, so that the query is only sent to the peers
that are able to answer it. It is composed of a set
of semantic related terms on a specific
knowledge domain.
In order to deliver these vocabularies to the
users, the system adopts a specific strategy. It is
managed by the service CVDS that is available in
ROSA portal. This service consists in storing all the
controlled vocabularies in this portal, external to the
P2P environment, so that whenever a peer connects
to the system for the first time it can receive them
via network.
4.3.2 Query Processing
According to Arenas (Arenas et al., 2003), query
processing is the most important service in a P2P
network, consisting basically of the query
distribution among peers. In the context of ROSA -
P2P system, query execution uses the query
execution machine named MEC ROSA (Coutinho
WEBIST 2006 - INTERNET TECHNOLOGY
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and Porto, 2004). Being responsible for the ROSA
algebra implementation, it is composed of a set of
operators to manipulate ROSA data such as: select,
project, browse, join and transitive closure.
However, as ROSA was initially built as a local
system, MEC ROSA access was restricted to a
single database, hence not allowing for the
generation of a query distributed plan, which would
be the most adequate to the ROSA - P2P distributed
environment. Thus, it was necessary to define a
strategy to use MEC ROSA in the system, which
will be described later in this section.
As query process in ROSA - P2P takes into
account a rich semantic context, it uses a particular
query processing strategy, executed according to the
following steps:
i) Sending queries
It consists in transmitting a query submitted by a
peer to other peers in the system, making it possible
this query can only be executed and answered by the
peers concerning a query domain. Therefore, the
main strategy adopted to optimize query processing
was firstly locating the relevant super-peers related
to the query domain.
Thus, when a query is submitted to the system,
the query processing initially verifies if the metadata
descriptors “title” and/or “keywords” are included in
the query. If it is the case, these values are compared
to the existing terms included in the keywords
controlled vocabulary. Once matched, the
corresponding subjects are then returned and the
query is sent to the peers associated to that
knowledge domain. In case these terms are not part
of the query, or if their values are not located, not
providing for the query domain identification, a
message is sent to the user, asking him/her for one or
more terms to include some additional semantic
value to the query. Some terms examples are then
exhibited, so that the user can suggest a term to
identify the query domain, allowing it to be sent to
the appropriate super-peers.
ii) Rewriting queries
It consists in rewriting queries through the selection
and/or navigation operations, which are then
rewritten according to the information stored in the
global and local vocabularies. This way queries are
able to encompass a more extensive data universe,
making it possible that all possible answers can be
retrieved, independently of the way data have been
semantically stored.
Therefore, once the query processing verifies the
query contains a select operation including the
metadata “title”, it will compare the title value with
the other synonymous terms of the local controlled
vocabulary. If it matches, equivalent terms will be
retrieved, and the select operation will be rewritten,
i.e., these new terms will be added to the select
clause and linked through the disjunction operator
(or). In case there exist other metadata in the select
operation such as “aggregation level” (this metadata
descriptor identifies if the object is a program,
course or topic), they should also be included in the
query, concatenated by the operator “or” or “and”
and placed at the end of the sentence according to
the conjunctive normal form (Coutinho and Porto,
2004) used by the select operation. The use of the
operator “or” or “and” changes according to the
select operation initially defined by the user. If the
query does not contain the metadata descriptor
“title”, it is not rewritten.
The query rewriting process continues, this time
for the navigation operation. Once it is included in
the query, the query processing compares each of the
predicates declared in the query with the
synonymous stored in the controlled global
vocabulary. For each query predicate, its
corresponding equivalent predicates are retrieved
from the vocabulary and rewritten, and hence
forming a set of rewritten predicates, which are then
concatenated through the disjunction operator (or).
This operator, together with the “and” conjunction
and the “.” navigation operators are used to join all
sets of rewritten predicates. Finally, the navigation
(or browsing) operation rewriting will be complete
when the rewriting of all predicate sets is joined
within the same sentence. The following example
illustrates these procedures. Suppose a query defined
as: “Select the LOs titles generated by those that
comprehend and fundament other LOs whose “title”
is equal to distributed database and their
“aggregation level” is equivalent to course”.
Query:
select|LOs@lom/general/title =
distributed database and LOs@lom/general/
aggregation_level = course
browsing|LOs@(( comprehends and
fundaments ). generates )
project|LOs@lom/general/title
This query will be rewritten by the query
processing into a semantically richer query, defined
as:
Query:
select|LOs@lom/general/title =
distributed database or LOs@lom/general/title =
DDBMS and LOs@lom/general/
aggregation_level = course
INTEGRATING E-LEARNING OBJECTS IN A P2P SYSTEM
219
browsing|LOs@((((((comprehends or
consists_of) or (is_composed_of or
is_formed_of)) or (has or includes)) or
(encompasses or involves)) or contains) and
((is_prerequisite_for or fundaments) or (is
base_for or is_condition_for))).((generates or
creates) or (develops or produces))
project|LOs@lom/general/title
iii) Executing queries
One of the critical issues analyzed in this section
refers to MEC ROSA. As already commented in the
beginning of this section, it can only submit queries
to a single database, meaning that an optimized
distributed query plan, such as the one developed in
(Nejdl et al., 2002), cannot be generated. Therefore,
the solution adopted in this work considers that each
peer processes a query similarly as the requestor
peer, having the same autonomy over it. Hence, each
peer will be able to rewrite the query and to manage
its own processing, overcoming the lack of a
distributed plan. The processing strategy considers
two phases, as described next:
• First phase: consists in identifying, at query
submission or reception, the relevant peers and/or
super-peers able to answer it. Thus, from the point of
view of the query submission made by a user, the
query processing will analyze if the peer is a super-
peer or not. In affirmative case, it will verify if it is
able to answer the query. In positive case, it will
rewrite the query to itself, storing the result in cache.
Then it will resend the query to its own peers and
will locate, among the super-peers to which it refers
to, those that are relevant, in order to send it to them.
Otherwise, when the peer is not a super-peer, the
query processing will simply send the query directly
to the corresponding super-peer, which will be
responsible for resending the query in the system.
However, from the point of view of the query
reception made by a peer or super-peer, the query
processing will also analyze if the peer is a super-
peer or not. If it is the case, it will verify if the super-
peer that originally sent the query belongs to its
grouping. If it is true, it will not be necessary to
resend the query to the other grouping super-peers,
since this super-peer will indeed do this. However, it
is necessary the super-peer resends the query to its
own peers. In case the super-peer does not belong to
its subject grouping, it needs to resend the query to
its super-peers grouping and to its own peers. In both
cases, at the moment when a query is resent by a
peer or super-peer, two timers are activated: one will
estimate the time limit the system will wait for the
corresponding partial results; and the other refers to
the expected number of results, according to the
quantity of peers or super-peers to which the query
has been sent to (Brito, 2005);
• Second phase: consists in rewriting and
processing the query, returning the result to the
requestor peer or super-peer. Thus, after submitting
or receiving a query, the corresponding peer or
super-peer will be responsible for rewriting it. Once
rewritten, the query is processed by MEC ROSA,
and its results, from the query submission point of
view, will be kept in the respective peer or super-
peer cache. From the query reception point of view
(made by a peer or super-peer), results are sent to the
requestor peer (or super-peer), also remaining in
cache for future integration. These results will
remain there until the time limit defined by the query
processing is over, or until all results have been
returned; afterwards results will be integrated.
4.3.3 Data Integration
It consists in integrating all cache query results
returned from relevant peers and/or super-peers,
making it possible a correct global answer to be
returned to the user. As each peer and/or super-peer
has to identify, rewrite and process a submitted
query, the resolution of existing consistency
problems in this phase is the responsibility of each
peer, and not only of the query requestor peer.
Hence, each of the partial results returned to the
requestor peer (or super-peer) is already free of any
inconsistence, becoming ready for the system to
process their union. In fact, this union is equivalent
to the query result integration.
5 RELATED WORK
In the literature there are many works related to P2P
systems, each one defined according to its specific
characteristics and requirements. PeerDB, Hyperion,
Piazza, SeLeNe and Edutella are one of these
systems.
PeerDB (Ooi et al. 2003) provides content based
queries, mobile agents integration, and a schema
mapping strategy based on descriptive words, which
are the only available metadata information. The
focus on the Hyperion (Arenas et al., 2003) project
resides on the specification and management of
metadata in order to enable data coordination and
sharing between peers. Similarly to the others, the
Piazza (Tatarinov et al., 2003) project defines data
sharing between peers from its mapping schemas,
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besides having a specific results system that
recursively expands any relevant mapping for a
query, retrieving important data from peers.
The
SeLeNe (Self e-Learning Networks) project (Keenoy
et al. 2004) is based on a GRID service architecture
supported by metadata, which provides facilities for
discovering and sharing e-learning resources.
Edutella system (Nejdl et al. 2002) is characterized
for providing a multiple platform to extend, specify
and implement a metadata infrastructure in RDF for
P2P network, which provides the sharing didactic
resources among institutions.
In the context of ROSA - P2P, Edutella is the one
that deserves more attention since, to the best of our
knowledge, it is the only architecture based on
super-peers
. Hence, some of its characteristics were
essential to define important issues in ROSA - P2P,
such as routing indices and peers grouping.
Furthermore, the main difference between ROSA -
P2P and the other systems concerns the type of
distributed architecture used to provide
interoperability and data sharing on the Web. While
the others use schema mapping tables, P2P, and
GRID, ROSA - P2P profits from the computational
capacity of P2P architecture based on super-peers,
and a complex ontology based structure to process
and integrate queries results. Nevertheless, ROSA -
P2P presents some features not explored so far in the
other projects, such as a specific strategy for
grouping super-peers based on a subject domain, and
instance integration instead of schemas, since ROSA
does not provide a conceptual schema.
6 CONCLUSION
This paper presented the evolution of ROSA system
that has been transformed from local system into a
distributed one, named ROSA - P2P, now able to
provide global answers to user’s queries.
Throughout this paper two aspects have been
carefully emphasized: the P2P environment and data
integration, which specify among other
characteristics: details about this environment,
responsible for an adequate interoperability;
controlled vocabularies to support semantic
conflicts, broadcast and queries rewriting; a query
processing strategy based on queries contents; and
the strategy used to integrate ROSA - P2P LOs.
The system has been validated with intensive
tests presenting results in satisfactory time, taking
into account the number of peers and the domain
knowledge diversity they encompassed (Brito,
2005). As future work, we intend to: continue the
system evaluation work, using a more robust
platform; perform communication protocols
simulation using different network topologies and a
larger number of peers to ensure the real system
stability; and enrich the system with a query
distributed optimized plan, which will provide more
autonomy in query processing.
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