Pervasive computing applications are increasingly leveraging contextual information from several sources to provide users with behavior appropriate to the environment in which they reside. If these sources of contextual information are used and deployed in an ad hoc manner, however, they may provide overlapping functionality, fail to provide needed functionality, and require the use of inconsistent interfaces by applications. To overcome these problems, we introduce a Contextual Information Service that provides applications with contextual information via a virtual database. Unlike previous efforts, our service provides applications a consistent, lightweight, and powerful mechanism for obtaining contextual information, and includes explicit support for the on demand computation of contextual information. We show, via example applications and a Contextual Information Service prototype that we have implemented, how this approach can be used to allow proactive applications to adapt their behavior to match a user’s current environment.

Abstract. Time modeling is a crucial feature in many application domains. However, temporal information often is not crisp, but is uncertain, subjective and vague. This is particularly true when representing historical information, as historical accounts are inherently imprecise. Similarly, we conjecture that in the Semantic Web representing uncertain temporal information will be a common requirement. Hence, existing approaches for temporal modeling based on crisp representation of time cannot be applied to these advanced modeling tasks. To overcome these difficulties, in this paper we present fuzzy interval-based temporal model capable of representing imprecise temporal knowledge. Our approach naturally subsumes existing crisp temporal models, i.e. crisp temporal relationships are intuitively represented in our system. Apart from presenting the fuzzy temporal model, we discuss how this model is integrated with the ontology model to allow annotating ontology definitions with time specifications. 1

Ubiquitous computing applications are increasingly leveraging contextual information from several sources to provide users with behavior appropriate to the environment in which they reside. If these sources of contextual information are used and deployed in an ad hoc manner, however, they may provide overlapping functionality, fail to provide needed functionality, and require the use of inconsistent interfaces by applications. To overcome these problems, we introduce a concise organization of services and a single service interface that provide applications with contextual information in a unified manner. We show, via example applications and services that we have implemented, how our service organization and interface can be used to allow proactive applications to adapt their behavior to match a user's current environment.

Abstract There are many applications where the precise time at which an event will occur (or has occurred) is uncertain. Temporal probabilistic logic programs (TPLPs) allow a programmer to express knowledge about such events. In this paper, we develop a model theory, fixpoint theory, and proof theory for TPLPs, and show that the fixpoint theory may be used to enumerate consequences of a TPLP in a sound and complete manner. Likewise the proof theory provides a sound and complete inference system. Last, but not least, we provide complexity results for TPLPs, showing in particular, that reasonable classes of TPLPs have polynomial data complexity. 1 Introduction There are a vast number of applications where uncertainty and time are indelibly intertwined. For example, the US Postal Service (USPS) as well as most commercial shippers have detailed statistics on how long shipments take to reach their destinations. Likewise, we are working on a Viennese historical land deed application where the precise time at which certain properties passed from one owner to another is also highly uncertain. Historical radio carbon dating methods are yet another source of uncertainty, providing approximate information about when a piece was created. Logical reasoning in situations involving temporal uncertainty is definitely important. For example, an individual querying the USPS express mail tracking system may want to know when he can expect his package to be delivered today-- he may then choose to stay home during the period when the probability of delivery seems very high, and leave a note authorizing the delivery official to leave the package by the door at other times.

There are numerous applications where one agent a needs to reason about the beliefs of another agent, as well as about the actions that other agents may take. Eiter, Subrahmanian, and Pick (1998) introduced the concept of an agent program, and provided a language within which the operating principles of an agent could be declaratively encoded on top of imperative data structures. We first introduce certain belief data structures that an agent needs to maintain. Then we introduce the concept of a Meta Agent Program (map), that extends the (Eiter, Subrahmanian, and Pick 1998) framework, so as to allow agents to peform metareasoning. We build a formal semantics for maps, and show how this semantics supports not just beliefs agent a may have about agent b's state, but also beliefs about agents b's beliefs about agent c's actions, beliefs about b's beliefs about agent c's state, and so on. Finally, we provide a translation that takes any map as input and converts it into an agent program s...

Robust and scalable user context management is the key enabler for the emerging context- and situation-aware applications, and ontology-based approaches have shown their usefulness for capturing especially context information on a high level of abstraction. But so far the problem has not been approached as a data management problem, which is key to scalability and robustness. The specific challenges lie in the imperfection of high-level context information, its time-dependence and the variability in the dynamics between its different elements.

Radio-Frequency Identification (RFID) technology is increasingly being used to support various industrial and ubiquitous computing applications. Although this technology holds the promise to facilitate many of our every day activities, the noisy and low-level data produced by RFID readers today is extremely difficult to use or comprehend in most but the simplest settings. In this paper, we present PEEX, a system that enables applications to easily define, extract, and manage meaningful probabilistic highlevel events from low-level RFID data. By using a declarative query language, the system simplifies definitions of new events. By using probabilities, the system copes with the noise and errors in the data and the inherent ambiguity in the event extraction. We have built PEEX as a layer on top of a traditional RDBMS, thus enabling applications not only to detect events but also manage them further as necessary. Through experiments with RFID traces collected on a real, building-wide RFID deployment, we demonstrate the performance and practicality of PEEX. 1.

Abstract — Recently the academic communities have paid more attention to the queries and mining on uncertain data. In the tasks such as clustering or nearest-neighbor queries, expected distance is often used as a distance measurement among uncertain data objects. Traditional database systems store uncertain objects using their expected (average) location in the data space. Distances can be calculated easily from the expected locations, but it poorly approximates the real expected distance values. Recent research work calculates the expected distance by calculating the weighted average of the pair-wise distances among samples of two uncertain objects. However the pair-wise distance calculations take much longer time than the the former method. In this paper, we propose an efficient method Approximation by Single Gaussian (ASG) to calculate the expected distance by a function of the means and variances of samples of uncertain objects. Theoretical and experimental studies show that ASG has both advantages of the latter method’s high accuracy and the former method’s fast execution time. We suggest that ASG plays an important role in reducing computational costs significantly in query processing and various data mining tasks such as clustering and outlier detection. I.

Abstract. The groundswell for the `00s is imprecise probabilities. Whether the numbers represent the probable location of a GPS device at its next sounding, the inherent uncertainty of an individual expert's probability prediction, or the range of values derived from the fusion of sensor data, probability intervals became an important way of representing uncertainty. However, until recently, there has been no robust support for storage and management of imprecise probabilities. In this paper, we define the semantics of traditional query algebra operations of selection, projection, Cartesian product and join, as well as an operation of conditionalization, specific to probabilistic databases. We provide efficient methods for computing the results of these operations and show how they conform to probability theory.

Radio Frequency Identification (RFID) technology is increasingly being used to improve various industrial processes, such as supply-chain management. Successes of this technology in industrial settings are leading many to consider other uses of RFID, including user-oriented public deployments. However, the noisy, low-level data produced by RFID readers is almost impossible to use or comprehend in most but the simplest settings. We present PEEX (Probabilistic Event EXtractor), a system that manages probabilistic high-level events from imprecise and erroneous RFID data. PEEX allows users to define probabilistic events from lower-level events. By using probabilities, the system copes with the noise in the data and the inherent ambiguity in the event extraction. We have built PEEX as a layer on top of a traditional RDBMS. We demonstrate, through experiments with real RFID traces collected on a small antenna deployment, that PEEX significantly improves event detection rates compared with deterministic techniques, and provides applications a flexible trade-off between event recall and precision. 1