Alexander Löser

Super-peer-based routing and clustering strategies for RDF-based peer-to-peer networks

RDF-based P2P networks have a number of advantages compared with simpler P2P networks such as Napster, Gnutella or with approaches based on distributed indices such as CAN and CHORD. RDF-based P2P networks allow complex and extendable descriptions of resources instead of fixed and limited ones, and they provide complex query facilities against these metadata instead of simple keyword-based searches.In previous papers, we have described the Edutella infrastructure and different kinds of Edutella peers implementing such an RDF-based P2P network. In this paper we will discuss these RDF-based P2P networks as a specific example of a new type of P2P networks, schema-based P2P networks, and describe the use of super-peer based topologies for these networks. Super-peer based networks can provide better scalability than broadcast based networks, and do provide perfect support for inhomogeneous schema-based networks, which support different metadata schemas and ontologies (crucial for the Semantic Web). Furthermore, as we will show in this paper, they are able to support sophisticated routing and clustering strategies based on the metadata schemas, attributes and ontologies used. Especially helpful in this context is the RDF functionality to uniquely identify schemas, attributes and ontologies. The resulting routing indices can be built using dynamic frequency counting algorithms and support local mediation and transformation rules, and we will sketch some first ideas for implementing these advanced functionalities as well.

Semantic Overlay Clusters within Super-Peer Networks

When joining information provider peers to a peer-to-peer network, an arbitrary distribution is sub-optimal. In fact, clustering peers by their characteristics, enhances search and integration significantly. Currently super-peer networks, such as the Edutella network, provide no sophisticated means for such a ”semantic clustering” of peers. We introduce the concept of semantic overlay clusters (SOC) for super-peer networks enabling a controlled distribution of peers to clusters. In contrast to the recently announced semantic overlay network approach designed for flat, pure peer-to-peer topologies and for limited meta data sets, such as simple filenames, we allow a clustering of complex heterogeneous schemes known from relational databases and use advantages of super-peer networks, such as efficient search and broadcast of messages. Our approach is based on predefined policies defined by human experts. Based on such policies a fully decentralized broadcast- and matching approach distributes the peers automatically to super-peers. Thus we are able to automate the integration of information sources in super-peer networks and reduce flooding of the network with messages.

Information integration in schema-based peer-to-peer networks

Peer-to-peer (P2P) networks have become an important infrastructure during the last years. Using P2P networks for distributed information systems allows us to shift the focus from centrally organized to distributed information systems where all peers can provide and have access to information. In previous papers, we have described an RDF-based P2P infrastructure called Edutella which is a specific example of a more advanced approach to P2P networks called schema-based peer-to-peer networks. Schema-based P2P networks have a number of advantages compared with simpler P2P networks such as Napster or Gnutella. Instead of prescribing one global schema to describe content, they support arbitrary metadata schemas and ontologies (crucial for the Semantic Web). Thereby they allow complex and extendable descriptions of resources thus introducing dynamic behavior to the former fixed and limited descriptions, and can provide complex query facilities against these metadata instead of simple keyword-based searches. In this paper we will elaborate topologies, indices and query routing strategies for efficient query distribution in such networks. Our work is based on the concept of super-peer networks which provide better scalability compared to traditional P2P networks. By adapting existing concepts of mediator-based information systems to super-peer based networks, as we will showin this paper, they are able to support sophisticated routing, clustering and mediation strategies based on the metadata schemas and attributes. The resulting routing indices can be built using local clustering policies and support local mediation and transformation rules between heterogeneous schemas, and we sketch some first ideas for implementing these advanced functionalities as well.

Super-Peer-Based Routing Strategies for RDF-Based Peer-to-Peer Networks

RDF-based P2P networks have a number of advantages compared to simpler P2P networks such as Napster, Gnutella or to approaches based on distributed indices on binary keys such as CAN and CHORD. RDF-based P2P networks allow complex and extendable descriptions of resources instead of fixed and limited ones, and they provide complex query facilities against these metadata instead of simple keyword-based searches. In this paper we will discuss RDF-based P2P networks like Edutella as a specific example of a new type of P2P networks - schema-based P2P networks - and describe the use of super-peer based topologies for these networks. Super-peer based networks can provide better scalability than broadcast based networks, and provide support for inhomogeneous schema-based networks, with different metadata schemas and ontologies (crucial for the Semantic Web). Based on (dynamic) metadata routing indices, stated in RDF, the superpeer network supports sophisticated routing and distribution strategies, as well as preparing the ground for advanced mediation and clustering functionalities.

Semantic social overlay networks

Peer selection for query routing is a core task in peer-to-peer networks. Unstructured peer-to-peer systems (like Gnutella) ignore this problem, leading to an abundance of network traffic. Structured peer-to-peer systems (like Chord) enforce a particular, global way of distributing data among the peers in order to solve this problem, but then encounter problems of network volatility and conflicts with the autonomy of the peer data management. In this paper, we propose a new mechanism, INGA, which is based on the observation that query routing in social networks is made possible by locally available knowledge about the expertise of neighbors and a semantics-based peer selection function. We validate INGA by simulation experiments with different data sets. We compare INGA with competing peer selection mechanisms on resulting parameters like recall, message gain or number of messages produced.

Pricing Approaches for Data Markets

Currently, multiple data vendors utilize the cloud-computing paradigm for trading raw data, associated analytical services, and analytic results as a commodity good. We observe that these vendors often move the functionality of data warehouses to cloud-based platforms. On such platforms, vendors provide services for integrating and analyzing data from public and commercial data sources. We present insights from interviews with seven established vendors about their key challenges with regard to pricing strategies in different market situations and derive associated research problems for the business intelligence community.

Navigating the intranet with high precision

Despite the success of web search engines, search over large enterprise intranets still suffers from poor result quality. Earlier work [6] that compared intranets and the Internet from the view point of keyword search has pointed to several reasons why the search problem is quite different in these two domains. In this paper, we address the problem of providing high quality answers to navigational queries in the intranet (e.g., queries intended to find product or personal home pages, service pages, etc.). Our approach is based on offline identification of navigational pages, intelligent generation of term-variants to associate with each page, and the construction of separate indices exclusively devoted to answering navigational queries. Using a testbed of 5.5M pages from the IBM intranet, we present evaluation results that demonstrate that for navigational queries, our approach of using custom indices produces results of significantly higher precision than those produced by a general purpose search algorithm.

Semantic methods for p2p query routing

Knowledge sharing in a virtual organization requires a knowledge life cycle including knowledge provisioning, terminology alignment, determination of resource location, query routing, and query answering. In this talk we focus on the issue of determining a relevant resource in a completely decentralized setting such as necessitated by peer-to-peer knowledge management in virtual organizations. Requirements for this task include, e.g., full autonomy of peers as well as full control over own resources and therefore preclude prominent resource location and query routing schemes such as distributed hash tables. In order to tackle given requirements we use a resource location and query routing approach that exploits social metaphors of topical experts and experts’ experts as well as semantic similarity of queries and information sources. The approach has been fully tested in simulation runs and partially implemented in the system Bibster (http://bibster.semanticweb.org).

Near-duplicate detection for web-forums

Current forum search technologies lack the ability to identify threads with near-duplicate content and to group these threads in the search results. As a result, forum users are overloaded with duplicated search results and prefer to create new threads without trying to find existing ones. In this paper we therefore identify common reasons leading to near-duplicates and develop a new near-duplicate detection algorithm for forum threads. The algorithm is implemented using a large case study of a real-world forum serving more than one million users. We compare this work with current algorithms, similar to [4, 5], for detecting near-duplicates on machine generated web pages. Our preliminary results show, that we significantly outperform these algorithms and that we are able to group forum threads with a precision of 74%.

Robust recognition of complex entities in text exploiting enterprise data and NLP-techniques

Data transactions between business partners often include unstructured data such as invoices or purchase orders. In order to process such automatically, complex business entities need to be identified. Examples for complex entities are products, business partners and purchase orders which are stored in a supplier relationship management system. Both, structured records in the enterprise system and text data, describe these complex entities. A major challenge is to correctly associate entities recognized in unstructured data with entities stored in structured data, e.g. enterprise databases. We address that problem and propose a robust process methodology which includes three phases: candidate extraction from unstructured text, generation of initial mappings with structured data and disambiguation of the mappings exploiting relationships among the entities in the enterprise data and the documentspsila structure. We describe each step in detail, propose a common architecture and introduce to our data model and algorithms.