Argumentation frameworks (AFs) provide formal approaches for legal reasoning, but identifying sources of ambiguity and explaining argument acceptance remains challenging for non-experts. We present AF-XRAY, an open-source toolkit for exploring, analyzing, and visualizing abstract AFs in legal reasoning. AF-XRAY introduces: (i) layered visualizations based on game-theoretic argument length revealing well-founded derivation structures; (ii) classification of attack edges by semantic roles (primary, secondary, blunders); (iii) overlay visualizations of alternative 2-valued solutions on ambiguous 3-valued grounded semantics; and (iv) identification of critical attack sets whose suspension resolves undecided arguments. Through systematic generation of critical attack sets, AF-XRAY transforms ambiguous scenarios into grounded solutions, enabling users to pinpoint specific causes of ambiguity and explore alternative resolutions. We use real-world legal cases (e.g., Wild Animals as modeled by Bench-Capon) to show that our tool supports teleological legal reasoning by revealing how different assumptions lead to different justified conclusions.

We propose a new layered visualization in PyArg for grounded labelings of abstract argumentation frameworks. Argument nodes are colored according to their label (IN, OUT, or UNDEC) and have a new length annotation, which is derived from provenance subgraphs. New edge annotations explain an attack-edge’s role in determining the value (label) of nodes in an argumentation framework.

Scientific workflow systems are used to integrate existing software components (actors) into larger analysis pipelines to perform in silico experiments. Current approaches for handling data in nested-collection structures, as required in many scientific domains, lead to many record-management actors (shims) that make the workflow structure overly complex, and as a consequence hard to construct, evolve and maintain. By constructing and executing workflows from bioinformatics and geosciences in the Kepler system, we will demonstrate how COMAD (Collection-Oriented Modeling and Design), an extension of conventional workflow design, addresses these shortcomings. In particular, COMAD provides a hierarchical data stream model (as in XML) and a novel declarative configuration language for actors that functions as a middleware layer between the workflow's data model (streaming nested collections) and the actor's data model (base data and lists thereof). Our approach allows actor developers to focus on the internal actor processing logic oblivious to the workflow structure. Actors can then be re-used in various workflows simply by adapting actor configurations. Due to streaming nested collections and declarative configurations, COMAD workflows can usually be realized as linear data processing pipelines, which often reflect the scientific data analysis intention better than conventional designs. This linear structure not only simplifies actor insertions and deletions (workflow evolution), but also decreases the overall complexity of the workflow, reducing future effort in maintenance.

Many scientific workflow systems are built on dataflow-based models of computation in which data drives the execution of workflow components. An advantage of using dataflow models is their straightforward semantics (which includes support for branching, merging, and looping) and their ability to concurrently execute workflow steps. However, for many data-intensive workflows the dataflow model often requires data buffering. Current systems largely perform buffering through in-memory queues which can lead to buffer overflow and performance degradation as queues reach capacity (e.g., because of paging). We describe an alternative framework that leverages external storage to implement buffers (which we refer to as persistent queues) within data-intensive scientific workflows. Our framework can easily be used with different underlying storage technologies, and we consider and evaluate three distinct approaches: a traditional relational database implementation, a non-relational implementation designed for fast reads and writes, and a specialized approach that can further reduce external buffering overhead. In addition, the use of persistent queues can provide detailed provenance information "for free'' by capturing the input and output information of each workflow component during workflow execution. Although many systems provide such provenance information, we show how this information can be captured both efficiently and can be used to improve overall workflow performance through persistent queues.

Earth and environmental scientists collect and use a wide range of observational data. This data often exhibits high structural and semantic heterogeneity due to the variety of data collected and the ways in which observational datasets are structured in practice. However, to address questions at broad temporal, geographic, and biological scales, researchers often need to access and combine data from many observational datasets. This paper presents a system called ObsDB that helps to address these challenges by providing an integrated environment for storing, querying, and analyzing heterogeneous data based on a semantic observational model. The model allows for ontology-based descriptions of observational datasets and provides a common representation for storing observational data. The obsdb system is built on top of standard relational database technology and provides a declarative query language for accessing observations. Integrated support is also provided for exploratory data analysis, allowing users to call analytical scripts created using the R system over stored observational data.

A key advantage of scientific workflow systems over traditional scripting approaches is their ability to automatically record data and process dependencies introduced during workflow runs. This information is often represented through provenance graphs, which can be used by scientists to better understand, reproduce, and verify scientific results. However, while most systems record and store data and process dependencies, few provide easy-to-use and efficient approaches for accessing and querying provenance information. Instead, users formulate provenance graph queries directly against physical data representations (e.g., relational, XML, or RDF), leading to queries that are difficult to express and expensive to evaluate. We address these problems through a high-level query language tailored for expressing provenance graph queries. The language is based on a general model of provenance supporting scientific workflows that process XML data and employ update semantics. Query constructs are provided for querying both structure and lineage information. Unlike other languages that return sets of nodes as answers, our query language is closed, i.e., answers to lineage queries are sets of lineage dependencies (edges) allowing answers to be further queried. We provide a formal semantics for the language and present novel techniques for efficiently evaluating lineage queries. Experimental results on real and synthetic provenance traces demonstrate that our lineage based optimizations outperform an in-memory and standard database implementation by orders of magnitude. We also show that our strategies are feasible and can significantly reduce both provenance storage size and query execution time when compared with standard approaches.

This demonstration presents an interactive provenance browser for visualizing and querying data dependency (lineage) graphs produced by scientific workflow runs. The browser allows users to explore different views of provenance as well as to express complex and recursive graph queries through a high-level query language (QLP). Answers to QLP queries are lineage preserving in that queries return sets of lineage dependencies (denoting provenance graphs), which can be further queried and visually displayed (as graphs) in the browser. By combining provenance visualization, navigation, and query, the provenance browser can enable scientists to more easily access and explore scientific workflow provenance information.

Scientific workflows are increasingly used to rapidly integrate existing algorithms to create larger and more complex programs. However, designing workflows using purely dataflow-oriented computation models introduces a number of challenges, including the need to use low-level components to mediate and transform data (so-called shims) and large numbers of additional ¿wires¿ for routing data to components within a workflow. To address these problems, we employ Virtual Data Assembly Lines (VDAL), a modeling paradigm that can eliminate most shims and reduce wiring complexity. We show how a VDAL design can be implemented using existing XML technologies and how static analysis can provide significant help to scientists during workflow design and evolution, e.g., by displaying actor dependencies or by detecting so-called unproductive actors.

The amount of ecological data available electronically is increasing at a rapid rate, e.g., over 15,000 data sets are available today in the Knowledge Network for Biocomplexity (KNB) alone. Using the existing search capabilities of these online data repositories, however, scientists struggle to quickly locate data that are relevant to their needs or that will integrate with their current data sets. Semantic technologies aim at addressing many of these problems and hold the promise of enabling more powerful "smart" searches of online data archives. We describe new semantic search features within the Metacat meta-data system, which is used by many ecological research sites around the world for archiving their data using a standardized metadata format. Our semantic search sys-tem adds to Metacat the ability to store OWL-DL ontologies in addition to semantic annotations that link data set attributes to ontology terms. Our approach also extends Metacat to improve metadata search in multiple ways: (i) by expanding standard keyword searches with ontology term hierarchies; (ii) by allowing keyword searches to be applied to annotations in addition to traditional meta-data; and (iii) by allowing more structured searches over annotations via ontology terms. We describe our implementation of these extensions, and compare and contrast these different types of search for a corpus of annotated documents. As data repositories continue to grow, these tools will be instrumental in helping scientists precisely locate and then interpret data for their research needs.

Scientific workflow systems are increasingly used to automate complex data analyses, largely due to their benefits over traditional approaches for workflow design, optimization, and provenance recording. Many workflow systems employ a simple dependency model to represent the provenance of data produced by workflow runs. Although commonly adopted, this model does not capture explicit data dependencies introduced by "provenance-aware" processes, and it can lead to inefficient storage when workflow data is complex or structured. We present a provenance model, extending the conventional approach, that supports (i) explicit data dependencies and (ii) nested data collections. Our model adopts techniques from reference-based XML versioning, adding annotations for process and data dependencies. We present strategies and reduction techniques to store immediate and transitive provenance information within our model, and examine trade-offs among update time, storage size, and query response time. We evaluate our approach on real-world and synthetic workflow execution traces, demonstrating significant reductions in storage size, while also reducing the time required to store and query provenance information.

Abstract — XML process networks are a simple, yet power-ful programming paradigm for loosely coupled, coarse-grained dataflow applications such as data-centric scientific workflows. We describe a framework called ∆-XML that is well-suited for applications in which pipelines of data processors modify parts (“deltas”) of XML data collections while keeping the overall collection structure intact. We show how to optimize the execution of ∆-XML process networks by minimizing the data shipping cost in distributed settings. This X-CSR1 optimization employs static type inference based on XML Schema to determine the XML stream fragments that are relevant to a processor, allowing irrelevant fragments to be bypassed (“shipped”) to downstream pipeline steps. Finally, we present evaluation results for a real-world scientific workflow, which shows the practical feasibility of X-CSR. A long version of this paper is available as [1]. I.

Abstract We describe a provenance model tailored to scientific workflows based on the collection‐oriented modeling and design paradigm. Our implementation within the Kepler scientific workflow system captures the dependencies of data and collection creation events on preexisting data and collections, and embeds these provenance records within the data stream. A provenance query engine operates on self‐contained workflow traces representing serializations of the output data stream for particular workflow runs. We demonstrate this approach in our response to the first provenance challenge. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract Scientific workflows often benefit from or even require advanced modeling constructs, e.g. nesting of subworkflows, cycles for executing loops, data‐dependent routing, and pipelined execution. In such settings, an often overlooked aspect of provenance takes center stage: a suitable model of provenance (MoP) for scientific workflows should be based upon the underlying model of computation (MoC) used for executing the workflows. We can derive an adequate MoP from a MoC (such as Kahn's process networks) by taking into account the assumptions that a MoC entails, and by recording the observables which it affords. In this way, a MoP captures or at least better approximates ‘real’ data dependencies for workflows with advanced modeling constructs. As a specific instance, we elaborate on the Read–Write–ReSet model, a simple and flexible MoP suitable for a number of different MoCs. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract The first Provenance Challenge was set up in order to provide a forum for the community to understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a functional magnetic resonance imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarize the participants' contributions. Copyright © 2007 John Wiley & Sons, Ltd.

We view scientific workflows as the domain scientist's way to harness cyberinfrastructure for e-Science. Domain scientists are often interested in "end-to-end" frameworks which include data acquisition, transformation, analysis, visualization, and other steps. While there is no lack of technologies and standards to choose from, a simple, unified framework combining data modeling and processoriented modeling and design of scientific workflows has yet to emerge. Towards this end, we introduce a number of concepts such as models of computation and provenance, actor-oriented modeling, adapters, hybrid types, and higher-order components, and then outline a particular composition of some of these concepts, yielding a promising new synthesis for describing scientific workflows, i.e., Collection-Oriented Modeling and Design (COMAD).

People often impose new interpretations onto existing information. In the process, they work with information in two layers: a base layer, where the original information resides, and a superimposed layer, where only the new interpretations reside. Abstractions defined in the Superimposed Pluggable Architecture for Contexts and Excerpts (SPARCE) ease communication between the two layers. SPARCE provides three key abstractions for superimposed information management: mark, context, and excerpt. We demonstrate two applications, RIDPad and Schematics Browser, for use in the appeal process of the US Forest Service (USFS).

What do you do to make sense of a mass of information on a given topic? Paradoxically, you likely add yet more information to the pile: annotations, underlining, bookmarks, cross-references, etc. We want to build digital information systems for managing such added or superimposed information and support applications that create and manipulate it. We find that requirements for a superimposed information system can be quite different from those for a traditional database management system: a lightweight implementation, multi-model information structures, "schema-later" data entry, interacting with data that is "outside the box" (controlled by other applications), and support, rather than removal, of redundancy. We report on SLIMPad (Superimposed Layer Information Manager scratchPad), a superimposed application which was inspired by the "bundling" of information elements from disparate sources we observed in a medical setting. We propose an architecture for superimposed applications and information management. Our prototype components to implement the architecture give flexibility in structuring superimposed information, and also encapsulate addressing, at a sub-document granularity, into a variety of base information sources.

In the trade-offs between security and performance, it seems that security is always the loser. If we allow for adaptive security, we can at least ensure that security and performance are treated somewhat equally. Using adaptive security, we can allow a system to exist in a less secure, more performant state until it comes under attack. We then adapt the system to a more secure, less performant implementation. In this paper, we introduce the Security Adaptation Manager, or SAM. We describe SAM and how we have implemented SAM to take advantage of the different protection strengths offered by the StackGuard compiler. Using SAM to provide StackGuard-based adaptive security provides a form of misuse-based intrusion detection, capable of detecting known and novel attacks.