Computing devices and applications are now used beyond the desktop, in diverse environments, and this trend toward ubiquitous computing is accelerating. One challenge that remains in this emerging research field is the ability to enhance the behavior of any application by informing it of the context of its use. By context, we refer to any information that characterizes a situation related to the interaction between humans, applications, and the surrounding environment. Context-aware applications promise richer and easier interaction, but the current state of research in this field is still far removed from that vision. This is due to 3 main problems: (a) the notion of context is still ill defined, (b) there is a lack of conceptual models and methods to help drive the design of context-aware applications, and (c) no tools are available to jump-start the development of context-aware applications. In this anchor article, we address these 3 problems in turn. We first define context, identify categories of contextual information, and characterize context-aware application behavior. Though the full impact of context-aware computing requires understanding very subtle and high-level notions of context, we are focusing our efforts on the pieces of context that can be inferred automatically from sensors in a physical environment. We then present a conceptual framework that separates the acquisition and representation of context from the delivery and reaction to context by a context-aware application. We have built a toolkit, the Context Toolkit, that instantiates this conceptual framework and supports the rapid development of a rich space of context-aware applications. We illustrate the usefulness of the conceptual framework by describing a number of context-aware applications that have been prototyped using the Context Toolkit. We also demonstrate how such a framework can support the investigation of important research challenges in the area of context-aware computing.

/pdf/a-conceptual-framework-and-a-toolkit-for-supporting-the-3ct19ji2mn.pdf

A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications

One of the major applications of data mining is in helping companies determine which potential customers to market to. If the expected profit from a customer is greater than the cost of marketing to her, the marketing action for that customer is executed. So far, work in this area has considered only the intrinsic value of the customer (i.e, the expected profit from sales to her). We propose to model also the customer's network value: the expected profit from sales to other customers she may influence to buy, the customers those may influence, and so on recursively. Instead of viewing a market as a set of independent entities, we view it as a social network and model it as a Markov random field. We show the advantages of this approach using a social network mined from a collaborative filtering database. Marketing that exploits the network value of customers---also known as viral marketing---can be extremely effective, but is still a black art. Our work can be viewed as a step towards providing a more solid foundation for it, taking advantage of the availability of large relevant databases.

http://cs-people.bu.edu/evimaria/cs591/domingos01mining.pdf

Mining the network value of customers

https://www.hpl.hp.com/research/idl/papers/web10/fnn2.pdf

Friends and neighbors on the Web

An automated communications system operates to transfer data, metadata and methods from a provider computer to a consumer computer through a communications network. The transferred information controls the communications relationship, including responses by the consumer computer, updating of information, and processes for future communications. Information which changes in the provider computer is automatically updated in the consumer computer through the communications system in order to maintain continuity of the relationship. Transfer of metadata and methods permits intelligent processing of information by the consumer computer and combined control by the provider and consumer of the types and content of information subsequently transferred. Object oriented processing is used for storage and transfer of information. The use of metadata and methods further allows for automating may of the actions underlying the communications, including communication acknowledgements and archiving of information. Service objects and partner servers provide specialized data, metadata, and methods to providers and consumers to automate many common communications services and transactions useful to both providers and consumers. A combination of the provider and consumer programs and databases allows for additional functionality, including coordination of multiple users for a single database.

Computer-based communication system and method using metadata defining a control-structure

We describe a family of caching protocols for distrib-uted networks that can be used to decrease or eliminate the occurrence of hot spots in the network. Our protocols are particularly designed for use with very large networks such as the Internet, where delays caused by hot spots can be severe, and where it is not feasible for every server to have complete information about the current state of the entire network. The protocols are easy to implement using existing network protocols such as TCP/IP, and require very little overhead. The protocols work with local control, make efficient use of existing resources, and scale gracefully as the network grows. Our caching protocols are based on a special kind of hashing that we call consistent hashing. Roughly speaking, a consistent hash function is one which changes minimally as the range of the function changes. Through the development of good consistent hash functions, we are able to develop caching protocols which do not require users to have a current or even consistent view of the network. We believe that consistent hash functions may eventually prove to be useful in other applications such as distributed name servers and/or quorum systems.

/pdf/consistent-hashing-and-random-trees-distributed-caching-3h34vd88pi.pdf

Consistent hashing and random trees: distributed caching protocols for relieving hot spots on the World Wide Web

This paper discusses the design and performance of a hierarchical proxy-cache designed to make Internet information systems scale better. The design was motivated by our earlier trace-driven simulation study of Internet traffic. We challenge the conventional wisdom that the benefits of hierarchical file caching do not merit the costs, and believe the issue merits reconsideration in the Internet environment.

The cache implementation supports a highly concurrent stream of requests. We present performance measurements that show that our cache outperforms other popular Internet cache implementations by an order of magnitude under concurrent load. These measurements indicate that hierarchy does not measurably increase access latency. Our software can also be configured as a Web-server accelerator; we present data that our httpd-accelerator is ten times faster than Netscape's Netsite and NCSA 1.4 servers.

Finally, we relate our experience fitting the cache into the increasingly complex and operational world of Internet information systems, including issues related to security, transparency to cache-unaware clients, and the role of file systems in support of ubiquitous wide-area information systems.

/pdf/a-hierarchical-internet-object-cache-3l9wqukitw.pdf

A hierarchical internet object cache

It is increasingly difficult to make effective use of Internet information, given the rapid growth in data volume, user base, and data diversity. In this paper we introduce Harvest, a system that provides a scalable, customizable architecture for gathering, indexing, caching, replicating, and accessing Internet information.

http://codeontheroad.com/papers/Harvest.Conf.pdf

The Harvest information discovery and access system

In this paper we consider the problem of choosing among a collection of replicated servers, focusing on the question of how to make choices that segregate client/server traffic according to network topology. We explore the cost and effectiveness of a variety of approaches, ranging from those requiring routing layer support (e.g., anycast) to those that build location databases using application-level probe tools like traceroute. We uncover a number of tradeoffs between effectiveness, network cost, ease of deployment, and portability across different types of networks. We performed our experiments using a simulation parameterized by a topology collected from 7 survey sites across the United States, exploring a global collection of Network Time Protocol servers.

Locating nearby copies of replicated Internet servers

: Rapid growth in data volume user base and data diversity render Internet-accessible information increasingly difficult to use effectively. In this paper we introduce Harvest, a system that provides a set of customizable tools for gathering information from diverse repositories, building topic-specific content indexes, flexibly searching the indexes, widely replicating them, and caching objects as they are retrieved across the Internet. The system interoperates with Mosaic and with HTTP, FTP, and Gopher information resources. We discuss the design and implementation of each subsystem and provide measurements indicating that Harvest can reduce server load, network traffic and index space requirements significantly compared with previous indexing systems. We also discuss a half dozen indexes we have built using Harvest, underscoring both the customizability and scalability of the system.

/pdf/harvest-a-scalable-customizable-discovery-and-access-system-3resyjccni.pdf

Harvest: A Scalable, Customizable Discovery and Access System

ngoing increases in wide-area network connectivity promise vastly increased opportunities for collaboration and resource sharing. A fundamental problem confronting users of such networks i,; how to discover the existence of resources of interest, such as files, retail products, network services, or people. In tZhis article we focus on the problem of discovering people who have particular interests or expertise. For an overview of tlhe larger research project into which this work fits, the reader is referred to [16]. The typical approach to locating people is to build a directory from explicitly registered data. This approach is taken, for example, by the X.500 directory service standard [3]. While this approach provides good support for locating particular users (the \"white-pages\" problem), it does not: easily support finding users who have particular interests or expertise (the \"yellow-pages\" problem). One could create special interest group lists, but doing so requires a significant amount of effort. For each group someone has to build and maintain a membership list. Moreover, building such lists assumes one knows which lists should be compiled and who should be included in each list. In a large network, the set of possible interest groups can be quite large and rapidly evolving. It is difficult to track the interests of such a community using explicitly registered data. We consider a different approach, which deduces shared-interest relationships between people based on the history of email communication. Using this approach, a user could search J~or people by requesting a list of people whose interests are similar to several people known to have the interest in question. This technique can support a fine-grained, dynamic means of locating people with related interests. The set of possible interests can be arbitrarily specialized, and the people located will be appropriate at the time of the search, rather than at some earlier time when a list was compiled. One might at tempt to discern shared interests by analyzing subject lines and message bodies in electronic mail messages. Beyond the obvious privacy problems, doing this would pose difficult natura l l anguage recognition problems. Instead, we approached the problem by analyzing the structure of the graph formed from \"From:/To:\" email logs, using a set of heuristic graph algorithms. We demonstrate the algorithms by applying them to email logs we collected from 15 sites around the world between December 1, 1988 and January 31, 1989. The graph generated from these logs contained approximately 50,000 people in 3,700 different sites worldwide. Using these algorithms, we were able to deduce sharedinterest lists for people far beyond the data collection sites. Because the algorithms we present can deduce shared-interest relationships from any c o m m u n i c a t i o n graph, they are powerful and can threaten privacy. We propose recommendations that we believe should underlie the ethical use of these algorithms and discuss several possible applications we believe to not threaten privacy.

Michael F. Schwartz

Papers

A hierarchical internet object cache

The Harvest information discovery and access system

Locating nearby copies of replicated Internet servers

Harvest: A Scalable, Customizable Discovery and Access System

Discovering shared interests using graph analysis