objects do not contain such tags, so dedicated solutions like napster or gnutella ... Typically, these rely on a client-server architecture, where the metadata are ...
LOMster: Peer-to-peer Learning Object Metadata Stefaan Ternier, Erik Duval, Pieter Vandepitte Dept. Computerwetenschappen, Katholieke Universiteit Leuven, Belgium
Abstract This paper reports on the development of a peer-to-peer based tool to share and reuse learning objects, by making it easy to publish and search over their metadata [4]. The goal of this work is to leverage the popularity of file sharing applications such as gnutella and napster in the context of learning content.
Introduction The ARIADNE community has developed tools to facilitate the sharing and reusing of learning objects [8].This work is at the basis of the IEEE Learning Technologies Standardization Committee Learning Object Metadata (LOM)_ draft standard [4]. The evolution in peer-to-peer (P2P) networking [9], inspired us to examine the possibilities of P2P in the scope of share and reuse of learning objects. P2P technology has succesfully been applied to sharing music. Napster performs searches on mp3-tags which are incapsulated in an mp3-file. Learning objects do not contain such tags, so dedicated solutions like napster or gnutella (which searches on filenames) are not usable. In section 1, we will discuss briefly the LOM standard. The second section briefly introduces the advantages and disadvantages of P2P technology. The core of the paper is about a P2P solution to share and reuse learning objects. We conclude with a brief comparison of related work and a discussion of the current status of LOMster.
Learning Object Metadata In order to facilitate search and reuse of learning material, the IEEE LTSC is developing the Learning Object Metadata standard[4]. LOM specifies the structure of the meta-data that describes the learning object. At the time of writing, the LOM standard nears completion. There are many projects and infrastructures that rely on LOM [referenties naar papers over LOM-gebaseerde projecten in EdMedia 2000]. Typically, these rely on a client-server architecture, where the metadata are stored in a relational database, and web-based tools support inserting new material into the database, querying the database, downloading relevant learning objects, etc. As an example of a client-server based LOM infrastructure, the Ariadne Knowledge Pool System (KPS) is a star-shape distributed database. The user client interacts with the KPS through one of the local knowledge pools (LKP’s). Every LKP (Local Knowledge Pool) in the network replicates with a Regional Knowledge Pool, which in turn connects to Central Knowledge Pool. One problem with this approach is that replication between the RKP’s comes to a halt when the CKP crashes. However, this replication is not Figure 1: the Ariadne Knowledge Pool System critical, as the servers can replicate when the network load is low. If replication is down for a while, then this is not dramatic because replication can always happen at some later time. The LKPs are also acting as a server. A user who wants to query the knowledge pool uses our web-based query tool to query the database [5]. In the client/server model, the metadata client interacts with the LKP server. An LKP going down causes all local clients to become unusable.
Another problem often encountered with a client-server based LOM infrastructure, is that users often find it cumbersome to introduce new material into the repository of learning objects, an action that requires often quite detailed description of the content. In contrast, music sharing tools like Napster have demonstrated that many users are quite willing to share content (admittedly, often not their own!) if the actions involved are not more complicated than putting some files in some directory that is shared with the community involved.
Peer-to-peer Peer-to-peer networks are networks in which every computer performs the same tasks. This approach can be contrasted with a client/server based architecture, where the server provides dedicated services like file access, security enforcement, resource sharing, etc. Clients typically run on personal computers and access the services offered by the server to implement a meaningful interaction with the end user. The client/server model has its pros and cons. Because of the centralized approach, where every client connects to one server, user management is a relatively easy task. In this model users authenticate themselves to the server, so it’s easier to provide secure services. According to a Berekley attempt [7] to measure how much information is produced, the world publishes about 7.3 million internet pages each day. No single search engine can locate and collect this huge amout of information. In a peer-topeer environment, every peer publishes its own information. When a peer goes offline, only the information stored in the peer becomes unavailable. When performing a query, a peer queries its own information and sends the query to other peers which perform the same task. Somehow, the partial results from the different peers need to be merged and the result of the query is finally returned to the sender. A peer-to-peer network can cope with the problems associated to a client-server based approach that we outlined above. In a peer-to-peer network, all hosts interact as equals. There is no need for a central server which needs a lot of maintenance costs. As a consequence, a great advantage of a P2P approach is that the system becomes much more robust. When a peer gets disconnected from the network, only the information it manages itself becomes unavailable. File-sharing on peers is typically much slower then on servers. This can be blamed on the fact that there is no file system caching on peers and most of the peer services are running low priority. Another problem is one of scale. Knowledge pool servers can be replicated to fulfil scaling needs while peer-2peer networks tend to scale badly, because having all peers communicate with each other introduces a lot of overhead. Merging partial results (of queries) is not straightforward too. A peer can never decide when a result is satisfactory, because you never know whether a peer has all relevant results.
LOMster LOMster is a project that adresses sharing of learning objects on a P2P base with the following functionalities. 1. First of all, a user can select the learning objects he wants to share. To do so, a drag and drop feature will be provided. As mentioned before, the idea is that users may find this a more natural way to make their material available for share and reuse than uploading it to a central server, like the ARIADNE Knowledge Pool System. 2. When the user adds a file to the system, the tool generates as much metadata as possible (file size, media type, …). Next, the user can add additional metadata that could not be automatically generated. 3. Another feature is the ability to perform queries. A user can formulate search criteria over LOM fields. Complex boolean combinations of simple criteria are also supported. LOMster sends its query to all other known peers. From all these peers, a reply can be received. However, there is no guarantee that from a certain peer a result will be received as this peer can go offline before sending its reply. 4. Finally, a user can inspect the state of the current downloads and uploads.
To store the learning data we could not use an Oracle database as we do for the knowledge pool in the client/server version. Running such a database would impose too heavy a load on the typically rather simple user stations that LOMster is intended to run on, typically as a background process. Thus, we save all metadata in an XML file. This way, for each learning object there will be a separate XML-file that contains the metadata. In other words, publishing a learning object in LOMster leads to the creation of a LOM metadata file that is stored with the learning object itself. We choose to keep this metadata in an XML file because of widespread existence of XML parsers and query-tools. When queries are being processed, only the metadata are relevant. When the documents are downloaded, the user is primarily interested in the actual learning objects. To implement LOMster we used the JXTA [2,3] P2P Framework. JXTA is a framework defined by Sun Microsystems for developing P2P applications. To locate other peers, JXTA provides a publishing mechanism, where a peer can announce itself. These announcements are then broadcast to all other known peers. Now we can locate other peers, the next thing is to search for educational data. A searchmessage has 3 fields. 1. When a peer receives a search-message, the peerid-field tells him to which peer it has to peer-id send back its answers. query-id 2. The qid-field distinguishes old searches from query new ones. This is useful when a peer receives an answer and has to decide whether or not the Figure 2: structure of search-message answer is an answer to the last query. 3. Finally, the query-field contains the conditions to which answers have to satisfy. peer-id When an other LOMster peer receives the search query-id message, it processes the query in the search message. For each answer, it generates an answer message which also has 3 fields. . . . 1. The first field contains the peer-identifier of the peer that generated the answer. . . . 2. The query-identifier as mentioned above informs the receiver of the answer about which query is answered. 3. The last field describes the metadata that Figure 3: structure of answer-message belongs to the result of the query.
Related Work The Edutella project also adresses the share and reuse of learning objects. This open source project also uses the JXTA framework. Instead of LOM, a RDF-based metadata structure is used here.
Current Status and Future Work Current Status So far we have implemented a rudimentary prototype, based on the JXTA P2P platform. A user can manage it’s own upload/download directory. When dragging a file into the download directory, LOMster generates an associated XML file for LOM instance that describes the learning object. Figure 5 shows a screen shot of the LOMster tool.
Figure 4 : Screen Shot of LOMster
Future Work As we mentioned above, users often find it cumbersome to insert new material in a client-server based repository. We believe that a LOMster-like approach may help in overcoming this problem, as contributing new material now simply entails dropping the relevant files in the shared directory. Although this approach may lower the effort for making new material available, it also results in rather poor and limited metadata, unless the user still spends considerable effort on the detailed description of the learning objects involved. One way to tackle this problem is the semi-automatic generation of learning object metadata. At the simple and pragmatic level, this entails support for templates and reuse of existing metadata. At the intermediate level, simple processing of for instance HTML files could lead to reasonable automatically generated suggestions for metadata elements such as title, MIME type, file size, etc. At the more sophisticated level, Content-Based Retrieval (CBR) techniques could be used to generate some of the more semantically oriented metadata automatically too. We are currently investigating how such techniques can be integrated in LOMster, as well as in our more conventional client server based metadata environment [5].
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