Temporal Data Modeling and Reasoning for Information Systems

Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web

The Globe Infrastructure Directory Service

To implement adaptive replication strategies for Web documents, we have developed a wide area resource management system. This system allows servers to be managed on a local and global level. On a local level the system manages information about the resources and services provided by the servers, while on a global level the system allows servers to be searched for, added to, and removed from the system. As part of the system, and also in order to implement adaptive replication strategies, we introduce a hierarchical location representation for network elements such as servers, objects, and clients. This location representation allows us to easily and efficiently find and group network elements based on their location in a worldwide network. Our resource management system can be implemented using standard Internet technologies and has a broader range of applications besides making adaptive replication strategies possible for Web documents

A methodological framework for the analysis and design of Adaptive Web-based Information Systems

Due to the growing popularity and complexity of the Web, designing a web site is becoming a complex and difficult process, that needs to be supported by more powerful Web engineering methods. In this paper, we present our ongoing research on defining a methodological framework for the analysis and design of Adaptive Web-based Information Systems (AWIS). Adaptive systems use knowledge about a particular user, represented in the user model, to adapt their information , organization, and presentation to that user. Our approach is driven by an elicited set of High Level Users Goals, which allows intended users to come closer to satisfying their specific needs and preferences. It is composed of a number of process step guidelines along with their respective product models, that enables the Web designer to model his/her AWIS at different levels of abstraction. Thus, an AWIS is modeled at the conceptual level as modular adaptive views over the Information Domain, the conceptual schemas are then transformed into a logical level representation, which enables the actual implementation of the AWIS

Design for the contact zone. Knowledge management software and the structures of indigenous knowledges

This article examines the design of digital indigenous knowledge archives. In a discussion of the distinction between indigenous knowledge and western science, a decentred perspective is developed, in which the relationship between different local knowledges is explored. The particular characteristics of indigenous knowledges raise questions about if and how these knowledges can be managed. The role of technology in managing indigenous knowledges is explored with examples from fieldwork in India and Kenya and from web-based databases and digital archives. The concept of contact zone is introduced to explore the space in which different knowledges meet and are performed, such as indigenous knowledge and the technoscientific knowledge of the database. Design for the contact zone, this article proposes, is an intra-active and adaptive process for in creating databases that are meaningful for indigenous knowers. The meta-design approach is introduced as a methodology, which may provide indigenous knowers tools for self-representation and self-organisation through design

The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices

This paper proposes scalable and fast algorithms for solving the Robust PCA problem, namely recovering a low-rank matrix with an unknown fraction of its entries being arbitrarily corrupted. This problem arises in many applications, such as image processing, web data ranking, and bioinformatic data analysis. It was recently shown that under surprisingly broad conditions, the Robust PCA problem can be exactly solved via convex optimization that minimizes a combination of the nuclear norm and the $\ell^1$-norm . In this paper, we apply the method of augmented Lagrange multipliers (ALM) to solve this convex program. As the objective function is non-smooth, we show how to extend the classical analysis of ALM to such new objective functions and prove the optimality of the proposed algorithms and characterize their convergence rate. Empirically, the proposed new algorithms can be more than five times faster than the previous state-of-the-art algorithms for Robust PCA, such as the accelerated proximal gradient (APG) algorithm. Moreover, the new algorithms achieve higher precision, yet being less storage/memory demanding. We also show that the ALM technique can be used to solve the (related but somewhat simpler) matrix completion problem and obtain rather promising results too. We further prove the necessary and sufficient condition for the inexact ALM to converge globally. Matlab code of all algorithms discussed are available at http://perception.csl.illinois.edu/matrix-rank/home.htmlComment: Please cite "Zhouchen Lin, Risheng Liu, and Zhixun Su, Linearized Alternating Direction Method with Adaptive Penalty for Low Rank Representation, NIPS 2011." (available at arXiv:1109.0367) instead for a more general method called Linearized Alternating Direction Method This manuscript first appeared as University of Illinois at Urbana-Champaign technical report #UILU-ENG-09-2215 in October 2009 Zhouchen Lin, Risheng Liu, and Zhixun Su, Linearized Alternating Direction Method with Adaptive Penalty for Low Rank Representation, NIPS 2011. (available at http://arxiv.org/abs/1109.0367

Selection on stability across ecological scales

Much of the focus in evolutionary biology has been on the adaptive differentiation among organisms. It is equally important to understand the processes that result in similarities of structure among systems. Here, we discuss examples of similarities occurring at different ecological scales, from predator–prey relations (attack rates and handling times) through communities (food-web structures) to ecosystem properties. Selection among systemic configurations or patterns that differ in their intrinsic stability should lead generally to increased representation of relatively stable structures. Such nonadaptive, but selective processes that shape ecological communities offer an enticing mechanism for generating widely observed similarities, and have sparked new interest in stability properties. This nonadaptive systemic selection operates not in opposition to, but in parallel with, adaptive evolution

Personalization in cultural heritage: the road travelled and the one ahead

Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed