Ex Ante Evaluations of Alternate Data Structures for End User Queries

Selecting optimal instantiations of data models—Theory and validation of an ex ante approach

Fundamental notions of relative information capacity between database structures are studied in the context of the relational model. Four progressively less restrictive formal definitions of "dominance" between pairs of relational database schemata are given. Each of these is shown to capture intuitively appealing, semantically meaningful properties which are natural for measures of relative information capacity between schemata. Relational schemata, both with and without key dependencies, are studied using these notions. A significant intuitive conclusion concerns the informal notion of relative information capacity often suggested in the conceptual database literature, which is based on accessability of data via queries. Results here indicate that this notion is too general to accurately measure whether an underlying semantic connection exists between database schemata. Another important result of the paper shows that under any natural notion of information capacity equivalence, two relational schemata (with no dependencies) are equivalent if and only if they are identical (up to re-ordering of the attributes and relations). The approach and definitions used here can form part of the foundation for a rigorous investigation of a variety of important database problems involving data relativism, including those of schema integration and schema translation.

Introduction: Computational fluid dynamics (CFD) assess biological systems based on specific boundary conditions. We propose modeling more advanced hemodynamic metrics, such as core line length (CL) and critical points which characterize complexity of flow in the context of cerebral vasculature, and specifically cerebral veins during the physiologically evolving early neonatal state of vein of Galen malformations (VOGM). CFD has not been applied to the study of arteriovenous shunting in Vein of Galen Malformations but could help illustrate the pathophysiology of this malformation.Methods: Three neonatal patients with VOGM at Boston Children’s Hospital met inclusion criteria for this study. Structural MRI data was segmented to generate a mesh of the VOGM and venous outflow. Boundary condition flow velocity was derived from PC-MR sequences with arterial and venous dual velocity encoding. The mesh and boundary conditions were applied to model the cerebral venous flow. We computed flow variables including mean wall shear stress (WSSmean), mean OSI, CL, and the mean number of critical points (nCrPointsmean) for each patient specific model. A critical point is defined as the location where the shear stress vector field is zero (stationary point) and can be used to describe complexity of flow.Results: The division of flow into the left and right venous outflow was comparable between PC-MR and CFD modeling. A high complexity recirculating flow pattern observed on PC-MR was also identified on CFD modeling. Regions of similar WSSmean and OSImean (<1.3 fold) in the left and right venous outflow channels of a single patient have several-fold magnitude difference in higher order hemodynamic metrics (> 3.3 fold CL, > 1.7 fold nCrPointsmean). Specifically, the side which developed JBS in each model had greater nCrPointsmean compared to the jugular bulb with no stenosis (VOGM1: 4.49 vs. 2.53, VOGM2: 1.94 vs. 0, VOGM3: 1 vs. 0). Biologically, these regions had subsequently divergent development, with increased complexity of flow associating with venous stenosis. Discussion: Advanced metrics of flow complexity identified in computational models may reflect observed flow phenomena not fully characterized by primary or secondary hemodynamic parameters. These advanced metrics may indicate physiological states that impact development of jugular bulb stenosis in VOGM.

Ontologies represents main building material for creation of Semantic Web. Without ontologies it could not be possible to enable machine processability of data on the Semantic Web. Data is usually stored inside relational databases that represent the backbone for dynamical web sites. That data represents the invisible Web, ignored by search engines and Web crawlers. This paper presents a way to extract semantics from relational database schema and create OWL ontology automatically. Because relational database schema can capture very limited semantics, ontologies are enriched with additional WordNet semantics.

The evolving Web has seen ever-growing use of structured data, thanks to the way it enhances information authoring, querying, visualization and sharing. To date, however, most structured data authoring and management tools have been oriented towards programmers and Web developers. End users have been left behind, unable to leverage structured data for information management and communication as well as professionals. In this paper, I will argue that many of the benefits of structured data management can be provided to end users as well. I will describe an approach and tools that allow end users to define their own schemas (without knowing what a schema is), manage data and author (not program) interactive Web visualizations of that data using the Web tools with which they are already familiar, such as plain Web pages, blogs, wikis and WYSIWYG document editors. I will describe our experience deploying these tools and some lessons relevant to their future evolution.

The wide exploitation of new techniques and systems for generating, collecting and storing data has made available growing volumes of information. Large quantities of such information are stored as free texts. The lack of explicit structure in free text is a major issue in the categorization of such kind of data for more effective and efficient information retrieval, search and filtering. The abundance of structured data is problematic too. Several databases are available, that contain data of the same type. Unfortunately, they often conform to different schemas, which avoids the unified management of even structured information. The Entity Resolution process plays a fundamental role in the context of information integration and management, aimed to infer a uniform and common structure from various large-scale data collections, with which to suitably organize, match and consolidate the information of the individual repositories into one data set. De-duplication is a key step of the Entity Resolution process, whose goal is discovering duplicates within the integrated data, i.e., different tuples that, as a matter of facts, refer to the same real-world entity. This attenuates the redundancy of the integrated data and, also, enables more effective information handling and knowledge extraction through a unified access to reconciled and de-duplicated data. Duplicate detection is an active research area that benefits from contributions from diverse research fields, such as, machine learning, data mining and knowledge discovery, databases as well as information retrieval and extraction. This chapter presents an overview of research on data de-duplication, with the goal of providing a general understanding and useful references to fundamental concepts concerning the recognition of similarities in very large data collections. For this purpose, a variety of state-of-the-art approaches to de-duplication is reviewed. The discussion of the state-of-the-art conforms to a taxonomy that, at the highest level, divides the existing approaches into two broad classes, i.e., unsupervised and supervised approaches. Both classes are further divided into sub-classes according to the common peculiarities of the involved approaches. The strengths and weaknesses of each group of approaches are presented. Meaningful research developments to further advance the current state-of-the-art are covered as well.

8 - Types of metric

Indexing is an important process in Information Retrieval (IR) systems. It forms the core functionality of the IR process since it is the first step in IR and assists in efficient information retrieval. Indexing reduces the documents to the informative terms contained in them. It provides a mapping from the terms to the respective documents containing them. Once effective index has been built for the collection of documents, retrieval process is simplified. Indexing proceeds at four stages namely content specification, tokenization of documents, processing of document terms, and index building. The index can be stored in the form of different data structures namely direct index, document index, lexicon and inverted index. Index can be built by applying different algorithms or schemes such as single-pass in-memory indexing, blocked-indexing, etc. This paper explains the indexing process with the various data structures and algorithms used for indexing and finally analyses the different indexing approaches with respect to time, memory usage and mean average precision.

MotivationIndexing reference sequences for search—both individual genomes and collections of genomes—is an important building block for many sequence analysis tasks. Much work has been dedicated to developing full-text indices for genomic sequences, based on data structures such as the suffix array, the BWT and the FM-index. However, the de Bruijn graph, commonly used for sequence assembly, has recently been gaining attention as an indexing data structure, due to its natural ability to represent multiple references using a graphical structure, and to collapse highly-repetitive sequence regions. Yet, much less attention has been given as to how to best index such a structure, such that queries can be performed efficiently and memory usage remains practical as the size and number of reference sequences being indexed grows large.ResultsWe present a novel data structure for representing and indexing the compacted colored de Bruijn graph, which allows for efficient pattern matching and retrieval of the reference information associated with each k-mer. As the popularity of the de Bruijn graph as an index has increased over the past few years, so have the number of proposed representations of this structure. Existing structures typically fall into two categories; those that are hashing-based and provide very fast access to the underlying k-mer information, and those that are space-frugal and provide asymptotically efficient but practically slower pattern search. Our representation achieves a compromise between these two extremes. By building upon minimum perfect hashing and making use of succinct representations where applicable, our data structure provides practically fast lookup while greatly reducing the space compared to traditional hashing-based implementations. Further, we describe a sampling scheme for this index, which provides the ability to trade off query speed for a reduction in the index size. We believe this representation strikes a desirable balance between speed and space usage, and allows for fast search on large reference sequences.Finally, we describe an application of this index to the taxonomic read assignment problem. We show that by adopting, essentially, the approach of Kraken, but replacing k-mer presence with coverage by chains of consistent unique maximal matches, we can improve the space, speed and accuracy of taxonomic read assignment.Availability and implementationpufferfish is written in C++11, is open source, and is available at https://github.com/COMBINE-lab/pufferfish.Supplementary informationSupplementary data are available at Bioinformatics online.

A Stakeholder's Communication Approach for Balancing Hospital Information Systems Investment Priorities

Purpose. The authors aim to determine the differences between functional programming languages, to identify the capabilities of the most popular languages by comparing and analyzing them. To identify the main features, it is necessary to consider their data structures, as well as the application scope. The authors also aim to analyze and compare examples from various fields of language application using metrics of the program texts complexity. Methodology. The five most popular specialized functional languages are selected: Erlang, Lisp, F #, Scala and Haskel. An overview of the official documentation was conducted to obtain information on the capabilities of each language; their data structures and the application scope were studied. The experimental research base is formed from texts of the existing open source software systems and matched by similar applications and equal volume of text. Comparative analysis of sample programs is performed using Halsted metrics, which are calculated using specially designed software. The analysis of the received quality assessments is done graphically. Findings. Software has been developed to obtain Halsted metrics for program input texts in functional programming languages such as Erlang, Lisp, F # and Scala. The complexity of the Haskel programming language syntax did not allow the use of metrics to evaluate the text, so only a documentation review was performed. Benchmarking shows the differences between languages and outlines their use. The examples of different volumes from such areas of application as system programming tasks, graphing, mathematical calculations, AI systems, web programming, etc. were compared. Originality. The authors first conducted a comparative analysis of specialized languages using text complexity metrics, which made it possible to establish that Lisp has the smallest vocabulary and code length, Scala text has the most structured form, and F # and Erlang are marked with extra verbosity. Practical value. The findings and measurements will help in selecting the most effective functional programming language for solving specific problems, taking into account differences in applications. The developed software allows making measurements for various program texts when developing and maintaining complex software systems.

The rapid growth of available data arises the need for more sophisticated techniques for semantic access to information. It has been proved that using conceptual model or ontology over relational data sources is necessary to overcome many problems related with accessing the structured data. However, the task of wrapping the data residing in a database by means of an ontology is mainly done manually.The research we are carrying out studies the reuse of relational sources in the context of semantics-based access to information. This problem is tackled in two phases: (i) extracting semantics hidden in the relational sources by wrapping them by means of an ontology, (ii) understanding the methodology for semantic extension of such ontologies. In this paper we focus on the first sub-problem and present an automatic procedure for extracting from a relational database schema its conceptual view. The semantic mapping between the database and its conceptualization is captured by associating views over the data source to elements of the extracted ontology. To represent the extracted conceptual model we use an ontology language, rather that a graphical notation, in order to provide precise formal semantics. Our approach uses heuristics based on ideas of standard relational schema design and normalization. Under this we formally prove that our technique preserves the semantics of constraints in the database. Therefore, there is no data loss, and the extracted model constitutes a faithful wrapper of the relational database.

Expanded design procedures for learnable, usable interfaces (panel session)

Mapping relational database schema to ontology is a critical task for ontology-based heterogeneous database integration. This paper proposes a hybrid approach combining element-level and structure-level techniques to discover the correspondences between relational database schema and ontology. In the process of element-level matching, string-based and Word Net sense-based techniques are combined to calculate the name similarity between elements. This paper analyzes different characteristics exhibited by matching element pairs of various types and describes how to use these characteristics to calculate the structure similarity. A series of experiments indicate that our approach performs better than the previous ones.

End user computing (EUC) has become an essential component of successful knowledge management. To maintain their competitive advantage, organisations need to ensure that EUC is productive. Consequently, the problems encountered by end users in composing queries need to be minimised. In constructing queries, end users need to identify the columns, constraints, and aggregation level. They must then accurately translate these requirements into the appropriate constructs in the query language. The accurate translation of each requirement poses a challenge to end users due to the ontological gap between the representation of concepts in the real world and constructs available in the information system. This research examines the factors that influence end user performance in addressing SQL challenges. Ontology and cognitive psychology literature relating to memory schemas and semantic cues provide the theoretical basis for this research. A laboratory experiment was conducted with sixty-six students enrolled in introductory database subjects. The results demonstrate that end user familiarity with a particular SQL challenge has a positive relationship with query performance. The presence of semantic cues in the information request also has a substantial impact on query performance. These results provide assistance relative to improving end user training and designing query interfaces. Making these improvements will enhance both the efficiency and effectiveness of EUC.