There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human-andmachine-consumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.

/pdf/dbpedia-a-nucleus-for-a-web-of-open-data-64wla3p0zr.pdf

DBpedia: a nucleus for a web of open data

Managing trust is a problem of particular importance in peer-to-peer environments where one frequently encounters unknown agents. Existing methods for trust management, that are based on reputation, focus on the semantic properties of the trust model. They do not scale as they either rely on a central database or require to maintain global knowledge at each agent to provide data on earlier interactions. In this paper we present an approach that addresses the problem of reputation-based trust management at both the data management and the semantic level. We employ at both levels scalable data structures and algorithms that require no central control and allow to assess trust by computing an agents reputation from its former interactions with other agents. Thus the meethod can be implemented in a peer-to-peer environment and scales well for very large numbers of participants. We expect that scalable methods for trust management are an important factor, if fully decentralized peer-to-peer systems should become the platform for more serious applications than simple file exchange.

/pdf/managing-trust-in-a-peer-2-peer-information-system-rlboch6j0d.pdf

Managing trust in a peer-2-peer information system

Peer-To-Peer systems are driving a major paradigm shift in the era of genuinely distributed computing. Gnutella is a good example of a Peer-To-Peer success story: a rather simple software enables Internet users to freely exchange files, such as MP3 music files. But it shows up also some of the limitations of current P2P information systems with respect to their ability to manage data efficiently. In this paper we introduce P-Grid, a scalable access structure that is specifically designed for Peer-To-Peer information systems. P-Grids are constructed and maintained by using randomized algorithms strictly based on local interactions, provide reliable data access even with unreliable peers, and scale gracefully both in storage and communication cost.

P-Grid: A Self-Organizing Access Structure for P2P Information Systems

Peer-To-Peer systems are driving a major paradigm shift in the era of genuinely distributed computing. Gnutella is a good example of a Peer-To-Peer success story: a rather simple software enables Internet users to freely exchange files, such as MP3 music files. But it shows up also some of the limitations of current P2P information systems with respect to their ability to manage data efficiently. In this paper we introduce P-Grid, a scalable access structure that is specifically designed for Peer-To-Peer information systems. P-Grids are constructed and maintained by using randomized algorithms strictly based on local interactions, provide reliable data access even with unreliable peers, and scale gracefully both in storage and communication cost. Keywords: Peer-To-Peer computing, Distributed Indexing, Distributed Databases, Randomized Algorithms.

A self-organizing access structure for P2P information systems

1 Self-organizing Structured P2P Systems In the P2P community a fundamental distinction is made among unstructured and structured P2P systems for resource location. In unstructured P2P systems in principle peers are unaware of the resources that neighboring peers in the overlay networks maintain. Typically they resolve search requests by flooding techniques. Gnutella [9] is the most prominent example of this class. In contrast, in structured P2P systems peers maintain information about what resources neighboring peers offer. Thus queries can be directed and in consequence substantially fewer messages are needed. This comes at the cost of increased maintenance efforts during changes in the overlay network as a result of peers joining or leaving. The most prominent class of approaches to structured P2P systems are distributed hash tables (DHT), for example Chord [17]. Unstructured P2P systems have generated substantial interest because of emergent globalscale phenomena. For example, the Gnutella overlay network exhibits the following characteristics [15]: 1. The network has a small diameter, which ensures that a message flooding approach for search works with a relatively low timeto-life (approximately 7). 2. The node degrees of the overlay network follow a power-law distribution. Thus few peers have a large number of incoming links whereas most peers have a very low number of such links. These properties result from the way Gnutella performs network maintenance: each peer maintains a fixed number of active links. Using the network maintenance protocol a peer discovers new peers in the network by flooding discovery

/pdf/p-grid-a-self-organizing-structured-p2p-system-2pklrmdy65.pdf

P-Grid: a self-organizing structured P2P system

With the price of wireless sensor technologies diminishing rapidly we can expect large numbers of autonomous sensor networks being deployed in the near future. These sensor networks will typically not remain isolated but the need of interconnecting them on the network level to enable integrated data processing will arise, thus realizing the vision of a global "sensor Internet." This requires a flexible middleware layer which abstracts from the underlying, heterogeneous sensor network technologies and supports fast and simple deployment and addition of new platforms, facilitates efficient distributed query processing and combination of sensor data, provides support for sensor mobility, and enables the dynamic adaption of the system configuration during runtime with minimal (zero-programming) effort. This paper describes the global sensor networks (GSN) middleware which addresses these goals. We present GSN's conceptual model, abstractions, and architecture, and demonstrate the efficiency of the implementation through experiments with typical high-load application profiles. The GSN implementation is available from http://gsn.sourceforge.net/.

/pdf/infrastructure-for-data-processing-in-large-scale-2fqb991l3q.pdf

Karl Aberer

Papers

Managing trust in a peer-2-peer information system

P-Grid: A Self-Organizing Access Structure for P2P Information Systems

A self-organizing access structure for P2P information systems

P-Grid: a self-organizing structured P2P system

Infrastructure for Data Processing in Large-Scale Interconnected Sensor Networks