TcruziKB: Enabling Complex Queries for Genomic Data Exploration

We developed a novel analytical environment to aid in the examination of the extensive amount of interconnected data available for genome projects. Our focus is to enable flexibility and abstraction from implementation details, while retaining the expressivity required for post-genomic research. To achieve this goal, we associated genomics data to ontologies and implemented a query formulation and execution environment with added visualization capabilities. We use ontology schemas to guide the user through the process of building complex queries in a flexible Web interface. Queries are serialized in SPARQL and sent to servers via Ajax. A component for visualization of the results allows researchers to explore result sets in multiple perspectives to suit different analytical needs. We show a use case of semantic computing with real world data. We demonstrate facilitated access to information through expressive queries in a flexible and friendly user interface. Our system scores 90.54% in a user satisfaction evaluation with 30 subjects. In comparison with traditional genome databases, preliminary evaluation indicates a reduction of the amount of user interaction required to answer the provided sample queries

Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation

Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations.Comment: Software at https://github.com/lemire/OLAPTagClou

Coordinating visualizations of polysemous action: Values added for grounding proportion

We contribute to research on visualization as an epistemic learning tool by inquiring into the didactical potential of having students visualize one phenomenon in accord with two different partial meanings of the same concept. 22 Grade 4-6 students participated in a design study that investigated the emergence of proportional-equivalence notions from mediated perceptuomotor schemas. Working as individuals or pairs in tutorial clinical interviews, students solved non-symbolic interaction problems that utilized remote-sensing technology. Next, they used symbolic artifacts interpolated into the problem space as semiotic means to objectify in mathematical register a variety of both additive and multiplicative solution strategies. Finally, they reflected on tensions between these competing visualizations of the space. Micro-ethnographic analyses of episodes from three paradigmatic case studies suggest that students reconciled semiotic conflicts by generating heuristic logico-mathematical inferences that integrated competing meanings into cohesive conceptual networks. These inferences hinged on revisualizing additive elements multiplicatively. Implications are drawn for rethinking didactical design for proportions. © 2013 FIZ Karlsruhe

XML Schema Clustering with Semantic and Hierarchical Similarity Measures

With the growing popularity of XML as the data representation language, collections of the XML data are exploded in numbers. The methods are required to manage and discover the useful information from them for the improved document handling. We present a schema clustering process by organising the heterogeneous XML schemas into various groups. The methodology considers not only the linguistic and the context of the elements but also the hierarchical structural similarity. We support our findings with experiments and analysis

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

Automatic visualization and control of arbitrary numerical simulations

Authors’ preprint version as submitted to ECCOMAS Congress 2016, Minisymposium 505 - Interactive Simulations in Computational Engineering. Abstract: Visualization of numerical simulation data has become a cornerstone for many industries and research areas today. There exists a large amount of software support, which is usually tied to specific problem domains or simulation platforms. However, numerical simulations have commonalities in the building blocks of their descriptions (e. g., dimensionality, range constraints, sample frequency). Instead of encoding these descriptions and their meaning into software architecures we propose to base their interpretation and evaluation on a data-centric model. This approach draws much inspiration from work of the IEEE Simulation Interoperability Standards Group as currently applied in distributed (military) training and simulation scenarios and seeks to extend those ideas. By using an extensible self-describing protocol format, simulation users as well as simulation-code providers would be able to express the meaning of their data even if no access to the underlying source code was available or if new and unforseen use cases emerge. A protocol definition will allow simulation-domain experts to describe constraints that can be used for automatically creating appropriate visualizations of simulation data and control interfaces. Potentially, this will enable leveraging innovations on both the simulation and visualization side of the problem continuum. We envision the design and development of algorithms and software tools for the automatic visualization of complex data from numerical simulations executed on a wide variety of platforms (e. g., remote HPC systems, local many-core or GPU-based systems). We also envisage using this automatically gathered information to control (or steer) the simulation while it is running, as well as providing the ability for fine-tuning representational aspects of the visualizations produced

Automatic visualization and control of arbitrary numerical simulations

Authors’ preprint version as submitted to ECCOMAS Congress 2016, Minisymposium 505 - Interactive Simulations in Computational Engineering. Abstract: Visualization of numerical simulation data has become a cornerstone for many industries and research areas today. There exists a large amount of software support, which is usually tied to specific problem domains or simulation platforms. However, numerical simulations have commonalities in the building blocks of their descriptions (e. g., dimensionality, range constraints, sample frequency). Instead of encoding these descriptions and their meaning into software architecures we propose to base their interpretation and evaluation on a data-centric model. This approach draws much inspiration from work of the IEEE Simulation Interoperability Standards Group as currently applied in distributed (military) training and simulation scenarios and seeks to extend those ideas. By using an extensible self-describing protocol format, simulation users as well as simulation-code providers would be able to express the meaning of their data even if no access to the underlying source code was available or if new and unforseen use cases emerge. A protocol definition will allow simulation-domain experts to describe constraints that can be used for automatically creating appropriate visualizations of simulation data and control interfaces. Potentially, this will enable leveraging innovations on both the simulation and visualization side of the problem continuum. We envision the design and development of algorithms and software tools for the automatic visualization of complex data from numerical simulations executed on a wide variety of platforms (e. g., remote HPC systems, local many-core or GPU-based systems). We also envisage using this automatically gathered information to control (or steer) the simulation while it is running, as well as providing the ability for fine-tuning representational aspects of the visualizations produced

Schema Mapper: A Visualization Tool for DL Integration

Schema mapping is a challenging problem. It has come to the fore in recent years; there are important applications like database schema integration and, more recently, digital library merging of heterogeneous data. Previous studies have approached the schema mapping process either from algorithmic or visualization perspectives, with few integrating both. With Schema Mapper we demonstrate a semi-automatic tool for schema integration that combines a novel visual interface with an algorithm-based recommendation engine. Schemas are visualized as hyperbolic trees (see Fig. 1), thus allowing more schema nodes to be displayed at one time. Matches to selections are recommended to the user, which makes the mapping operation easier and faster

SchemaMapper: A tool for visualization of schema mapping

The world has changed significantly in the past few years with an increasing thrust towards the use of digital information. Every kind of application domain has found reasons to use digital information sources extensively. As a result, different types of data representation models or schemas have been developed. This poses a problem when there is a need for data integration from several sources. Diverse representations must be merged in order to create a single global representation. Hence there is a need for schema mapping tools that will enable amalgamation of heterogeneous data representations. That goal is difficult to achieve today since existing schema mapping tools are domain unaware. SchemaMapper, a new tool we have developed, tries to be domain aware and hence help speed up the schema mapping process. Further, it supports visualization of the mapping process by using a hyperbolic tree representation. This has not been used before in the context of schema mapping. Although the primary motivation for SchemaMapper comes from ETANA-DL (a digital library to promote integration of information and services from diverse archaeological sites), it can potentially be used in any other similar domains in the future, or further extended for different types of schema mappings. This report describes in detail the prototype developed for exploring the feasibility of such a tool, providing architecture and implementation details. Experiments were conducted to evaluate SchemaMapper and the initial results have been very encouraging. All the schemas used during the evaluation process were real life examples taken from ETANA-DL. Analysis of the evaluation results suggests that domain awareness is extremely useful for the schema mapping process. Also, the linear tree representation of schemas which existing tools use appears to have inherent disadvantages which need to be overcome in order to make the process more effective