Abstract not found

Abstract not found

Abstract not found

Abstract not found

Click is a new software architecture for building flexible and configurable routers. A Click router is assembled from packet processing modules called elements. Individual elements implement simple router functions like packet classification, queueing, scheduling, and interfacing with network devices. A router configuration is a directed graph with elements at the vertices; packets flow along the edges of the graph. Configurations are written in a declarative language that supports user-defined abstractions. This language is both readable by humans and easily manipulated by tools. We present language tools that optimize router configurations and ensure they satisfy simple invariants. Due to Click’s architecture and language, Click router configurations are modular and easy to extend. A standards-compliant Click IP router has sixteen elements on its forwarding path. We present extensions to this router that support dropping policies, fairness among flows, quality-of-service, and

This article presents a measure of semantic similarityinanis-a taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their e#ectiveness. 1. Introduction Evaluating semantic relatedness using network representations is a problem with a long history in arti#cial intelligence and psychology, dating back to the spreading activation approach of Quillian #1968# and Collins and Loftus #1975#. Semantic similarity represents a special case of semantic relatedness: for example, cars and gasoline would seem to be more closely related than, say, cars and bicycles, but the latter pair are certainly more similar. Rada et al. #Rada, Mili, Bicknell, & Blett...

This paper describes a technique for making personalized recommendations from any type of database to a user based on similarities between the interest profile of that user and those of other users. In particular, we discuss the implementation of a networked system called Ringo, which makes personalized recommendations for music albums and artists. Ringo's database of users and artists grows dynamically as more people use the system and enter more information. Four different algorithms for making recommendations by using social information filtering were tested and compared. We present quantitative and qualitative results obtained from the use of Ringo by more than 2000 people.

Bayou is a replicated, weakly consistent storage system designed for a mobile computing environment that includes portable machines with less than ideal network connectivity. To maximize availability, users can read and write any accessible replica. Bayou's design has focused on supporting apphcation-specific mechanisms to detect and resolve the update conflicts that naturally arise in such a system, ensuring that replicas move towards eventual consistency, and defining a protocol by which the resolution of update conflicts stabilizes. It includes novel methods for conflict detection, called dependency checks, and per-write conflict resolution based on client-provided merge procedures. To guarantee eventual consistency, Bayou servers must be able to rollback the effects of previously executed writes and redo them according to a global senalization order. Furthermore, Bayou permits clients to observe the results of all writes received by a server, Including tentative writes whose conflicts have not been ultimately resolved. This paper presents the motivation for and design of these mechanisms and describes the experiences gained with an initial implementation of the system.

The installation of high-speed networks using optical fiber and high bandwidth messsage forwarding gateways is changing the physical capabilities of information systems. These capabilities must be complemented with corresponding software systems advances to obtain a real benefit. Without smart software we will gain access to more data, but not improve access to the type and quality of information needed for decision making. To develop the concepts needed for future information systems we model information processing as an interaction of data and knowledge. This model provides criteria for a high-level functional partitioning. These partitions are mapped into information process-ing modules. The modules are assigned to nodes of the distributed information systems. A central role is assigned to modules that mediate between the users ' workstations and data re-sources. Mediators contain the administrative and technical knowledge to create information needed for decision-making. Software which mediates is common today, but the structure, the interfaces, and implementations vary greatly, so that automation of integration is awkward. By formalizing and implementing mediation we establish a partitioned information sys-

Data generated as a side effect of game play also solves computational problems and trains AI algorithms.