08181 Abstracts Collection -- The Evolution of Conceptual Modeling

From 24.04. to 30.04.2008, the Dagstuhl Seminar 08181 ``The Evolution of Conceptual Modeling\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Use of Subimages in Fish Species Identification: A Qualitative Study

Many scholarly tasks involve working with subdocuments, or contextualized fine-grain information, i.e., with information that is part of some larger unit. A digital library (DL) facil- itates management, access, retrieval, and use of collections of data and metadata through services. However, most DLs do not provide infrastructure or services to support working with subdocuments. Superimposed information (SI) refers to new information that is created to reference subdocu- ments in existing information resources. We combine this idea of SI with traditional DL services, to define and develop a DL with SI (SI-DL). We explored the use of subimages and evaluated the use of a prototype SI-DL (SuperIDR) in fish species identification, a scholarly task that involves work- ing with subimages. The contexts and strategies of working with subimages in SuperIDR suggest new and enhanced sup- port (SI-DL services) for scholarly tasks that involve working with subimages, including new ways of querying and search- ing for subimages and associated information. The main contribution of our work are the insights gained from these findings of use of subimages and of SuperIDR (a prototype SI-DL), which lead to recommendations for the design of digital libraries with superimposed information

Extending the 5S Framework of Digital Libraries to support Complex Objects, Superimposed Information, and Content-Based Image Retrieval Services

Advanced services in digital libraries (DLs) have been developed and widely used to address the required capabilities of an assortment of systems as DLs expand into diverse application domains. These systems may require support for images (e.g., Content-Based Image Retrieval), Complex (information) Objects, and use of content at fine grain (e.g., Superimposed Information). Due to the lack of consensus on precise theoretical definitions for those services, implementation efforts often involve ad hoc development, leading to duplication and interoperability problems. This article presents a methodology to address those problems by extending a precisely specified minimal digital library (in the 5S framework) with formal definitions of aforementioned services. The theoretical extensions of digital library functionality presented here are reinforced with practical case studies as well as scenarios for the individual and integrative use of services to balance theory and practice. This methodology has implications that other advanced services can be continuously integrated into our current extended framework whenever they are identified. The theoretical definitions and case study we present may impact future development efforts and a wide range of digital library researchers, designers, and developers

Adaptation Space: Surviving Non-Maskable Failures

Some failures cannot be masked by redundancies, because an unanticipated situation occurred, because fault-tolerance measures were not adequate, or because there was a security breach (which is not amenable to replication). Applications that wish to continue to offer some service despite nonmaskable failure must adapt to the loss of resources. When numerous combinations of non-maskable failure modes are considered, the set of possible adaptations becomes complex. This paper presents adaptation spaces, a formalism for navigating among combinations of adaptations. An adaptation space describes a collection of possible adaptations of a software component or system, and provides a uniform way of viewing a group of alternative software adaptations. Adaptation spaces describe the different means for monitoring the conditions that different adaptations depend on, and the particular configurations through which an adaptive application navigate. Our goal is to use adaptation spaces to provide survivable services to applications despite non-maskable failures such as malicious attacks. We present the basic concepts concerning adaptation spaces, with examples. We then present a formal model for reasoning about and selecting alternative adaptations, allowing developers of survivable application to automate their system’s adaptive behavior

Can Transportation Researchers Reuse Project Datasets and Programs?

Data users involved in research and analysis typically invest a lot of eort cleaning and manipu- lating their data as they work. Based on this observation, we have investigated two hypotheses: 1) reuse of datasets and procedures is difficult, and 2) the inability to reuse datasets and procedures is primarily due to a lack of documentation. To test these hypotheses we conducted structured interviews with data users asking questions regarding the struggles in their work pertaining to data, their documentation habits, and the importance of documentation. The interviews revealed that the data users rarely reused data or procedures, frequently encountered poor documentation, did not adequately document their own work, and unanimously agreed that documentation was important and that the absence of documentation was the primary cause for a lack of reuse. The results of these interviews have led us to develop a concept of a tool to help data users document and manage their work

A Generic Representation for Exploiting Model-Based Information

There are a variety of ways to represent information and each representation scheme typically has associated tools to manipulate it. In this paper, we present a single, generic representation that can accommodate a broad range of information representation schemes (i.e., structural models), such as XML, RDF, Topic Maps, and various database models.We focus here on model-based information where the information representation scheme prescribes structural modeling constructs (analogous to a data model in a database). For example, the XML model includes elements, attributes, and permits elements to be nested. Similarly, RDF models information through resources and properties. Having a generic representation for a broad range of structural models provides an opportunity to build generic technology to manage and store information. Additionally, we can use the generic representation to exploit a formally defined mapping language to transform information, e.g., from one scheme to another. In this paper, we present the generic representation and the associated mapping formalism to transform information and discuss some of the opportunities and challenges presented by this work

The Uni-Level Description: A Uniform Framework for Representing Information in Multiple Data Models

Abstract. One advantage of having several different representation schemes and data models is that users can select the right representation and associated tools for their particular need. However, multiple representations introduce structural, model-based heterogeneity, making it difficult to combine information from different sources and exploit information using generic tools (e.g., for querying or browsing). In this work, we define a uniform representation based on a meta-data-model called the Uni-Level Description (ULD) that can accommodate and accurately store information in a broad range of data models. The ULD defines three distinct instance-of relationships plus a relationship for modeling conformance, which is used to connect (data) constructs to other (schema) constructs and can be constrained to reflect the requirements of the data model. The ULD has been shown to enable powerful, generic transformation rules and simple generic browsing capability over information represented in diverse data models and representation schemes.