Complex Data Types Research Articles

Method for Automatically Generating Test Cases for Web Services

Because manual test case design is time-consuming and laborious and has certain blindness and inclination, Web service testing is based on WSDL document and formal model tree automatic generation. The theoretical research and Web service single operation test case generation method. Web service operation test case method, and to build test tools. The application results show that this method can greatly save the time of manually analyzing the complex data types in WSDL files and the time of generating the test cases according to the test cases of built-in data types. It simplifies the generation of test cases, and prepares test cases.

Reactome graph database: Efficient access to complex pathway data.

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types.

Warehousing and analysing textual data

Traditional data warehousing technologies have been widely used for simple data analysis. Nonetheless, they are not suitable for textual data. To deal with this type of complex data, new systems have been proposed. However, they only cover partially the complexity of textual data, which might therefore affect decision-making quality. This paper proposes an efficient approach for warehousing and analysing textual data. To tackle the main challenges facing these data, three major contributions are presented throughout our manuscript: i) a Semantic Text Cube Model (ST-Cube); ii) a new ETL approach (Extract Transform and Load); iii) an OLAP aggregation operator called Top_KRankedTopics. For the validation of our approach, we have developed and implemented a platform for the storage and the analysis of text documents. Experimental results show that our approach improves significantly the quality of information extracted from textual documents, which prove the effectiveness of our approach for textual data analysis.

Towards a completely extensible dynamic geometry software with metadata

Dynamic Geometry Software (DGS) are widely accepted as tools for creating and presenting visually rich interactive teaching and learning materials, called dynamic drawings. Dynamic drawings are specified by writing expressions in functional domain-specific languages. Due to wide acceptance of DGS, there has arisen a need for their extensibility, by adding new semantics and visuals. We have developed the SLGeometry dynamic geometry software with a genericized functional language and the corresponding expression evaluator that act as a framework into which specific semantics is embedded in the form of code annotated with metadata. SLGeometry is implemented in C# on the .NET Framework. Although attributes are a preferred mechanism to provide association of declarative information with C# code, they have certain restrictions which limit their application to representing complex structured metadata. By developing a metadata infrastructure which is independent of attributes, we were able to overcome these limitations. Our solution, presented in this work, provides extensibility to simple and complex data types, unary and binary operations, type conversions, functions and visuals, thus enabling developers to seamlessly add new features to SLGeometry by implementing them as C# classes annotated with metadata. It also provides insight into the way a domain specific functional language of dynamic geometry software can be genericized and customized for specific needs by extending or restricting the set of types, operations, type conversions, functions and visuals.

A Data Fusion Pipeline for Generating and Enriching Adverse Outcome Pathway Descriptions.

Increasing amounts of systems toxicology data, including omics results, are becoming publically available and accessible in databases. Data-driven and informatics-tool supported pipeline schemas for fitting such data into Adverse Outcome Pathway (AOP) descriptions could potentially aid the development of nonanimal-based hazard and risk assessment methods. We devised a 6-step workflow that integrated diverse types of toxicology data into a novel AOP scheme for pulmonary fibrosis. Mining of literature references and diverse data sources covering previous pathway descriptions and molecular results were coupled in a stepwise manner with informatics tools applications that enabled gene linkage and pathway identification in molecular interaction maps. Ultimately, a network of functional elements coupled 64 pulmonary fibrosis-associated genes into a novel, open-source AOP-linked molecular pathway, now available for commenting and improvements in WikiPathways (WP3624). Applying in silico-based knowledge extraction and modeling, the pipeline enabled screening and fusion of many different complex data types, including the integration of omics results. Overall, the taken, stepwise approach should be generally useful to construct novel AOP descriptions as well as to enrich developing AOP descriptions in progress.

CLAP, ACIR and SCOOP: Novel techniques for improving the performance of dynamic Metric Access Methods

Constant technological advances in electronic devices have led to the growth of elaborated data such as large texts, time series, georeferenced imagery, genetic sequences, photos, videos and several other types of complex data. Differently from scalar, traditional data types such as numbers and strings, complex data do not present the order relation property, which allows identifying whether an element precedes another according to some criterion. Therefore, these data are usually compared by the similarity degree among them. The Metric Access Methods (MAMs) are recognized as well-suited to perform similarity queries over such kind of data more efficiently than other access methods. MAMs can be considered dynamic or static depending on the pivot type used to construct them. Pivots are often employed to narrow the search for data. Global pivots can be employed to look into elements in the whole dataset, thus they have a high impact in the process of pruning irrelevant elements, since a single global pivot can be used to discard a large amount of irrelevant elements. Nevertheless, MAMs based on global pivots may have their dynamicity compromised by the fact that eventual pivot-related updates must be propagated through the entire structure. Local pivots, on the other hand, allow the maintenance to occur locally at the price of a lower pruning ability. In this paper, we propose novel techniques for improving the performance of dynamic MAMs without harming their dynamicity, once that several applications handle online complex data and, consequently, demand efficient dynamic indexes to be successful. Specifically, our main contributions are three techniques: (i) CLAP, which consists of employing local additional pivots to reduce distance calculations; (ii) ACIR, which is combined with CLAP and anticipates information from child nodes to reduce unnecessary disk accesses; and (iii) SCOOP, which is combined with CLAP as an extended version of ACIR, anticipating a larger amount of information from child nodes. The techniques have been applied to a dynamic MAM and evaluated over real datasets ranging from moderate to high dimensionality and cardinality. The experimental results show that our techniques were able to reduce query execution time in up to 63% for point queries and up to 53% for queries retrieving multiple elements.

Systematic Literature Review on Search Based Software Testing

The use of random search is very poor at finding solutions when those solutions occupy a very small part of the overall search space. Test data may be found faster and more reliably if the search is given some guidance. This work is a paper that explains the application of metaheuristic techniques in search-based software testing. The paper systematically review 47 papers selected randomly from online databases and conference proceeding based on the metaheuristic search techniques that have been most widely applied to problem solving, the different fitness function used for test data selection in each of the metaheuristic technique, and the limitation in the use of each search-based technique for software testing. It was found that GA outperformed its counterparts SA, HC, GP and random search approaches in generating test data automatically, different approaches were used to make sure that test data are selected within shorter period of time and also with wider coverage of the paths based on the fitness function, and most of the limitations of the articles are the handling of complex data types, like array, object types, and branch coverage. The paper also provides areas of possible future work on the use of metaheuristic techniques in search-based software testing.

Current status of pathway analysis in genome-wide association study.

Since the first publication in 2005, the genome-wide association study (GWAS) strategy has contributed significantly to the understanding of the mechanisms of human genetic diseases. Integrations of statistical methods and systematic biology are important means to explore the GWAS data. Pathway analysis establishes the importance of genetic variants from GWAS and provides insights into their biological significance. It is conducive in correlating the genetic variants, which have only small but interactive changes, to their importance in the biological pathways. At present, pathway analysis has been widely applied to studies of GWAS data, with relatively good results. In the meantime, various analytical methods are being developed and adapted for research on more types of complex data. In this review, we summarize the statistical methods of pathway analysis on GWAS data, and divide them into non-kernel methods and kernel methods. The non-kernel methods include gene set enrichment analysis (GSEA) and hierarchical Bayes prioritization (HBP) analysis, while kernel methods include linear kernel (LIN), identity-by-status kernel (IBS) and powered exponential kernel. We have summarized the calculation principles and features of these statistical methods to provide insights for further developments of new algorithms in GWAS research.

Abstract 2593: Accelerating pediatric brain tumor research through team science solutions

Abstract Introduction: The Children’s Brain Tumor Tissue Consortium (CBTTC), an international repository of genomic and phenotypic data, has partnered with Blackfynn, Inc., to create a cloud-based data management platform to facilitate team-science across disciplines. Background: The CBTTC through the CHOP Department of Biomedical and Health Informatics (DBHi) has developed a network of informatics and data applications for researchers across the globe to work together and perform real-time analyses on existing clinical, phenotypic, and genomic data. Historically, rare disease datasets are siloed, locked in proprietary formats, segregated by data types, and hidden from the view of experts in the field. This has been a significant barrier to finding effective therapeutics for children with pediatric brain tumors. Blackfynn was founded by a group of multidisciplinary experts in neuroscience, neurology, medicine, software development, engineering, computer science and business with the goal to empower researchers to cure neurologic disease and provide solutions to these challenges. Description of Methods: The CBTTC and Blackfynn teamed up to provide a cloud-based, team-focused data management and analytics platform. The platform provides a commercial grade, scalable approach to upload, view, and integrate digital pathology images with relevant subject data such as MRIs, pathology reports and genomic information. Stakeholders can search integrated data without requiring users to change their current workflow or conform to imposed data standards. This platform is a simple, intuitive, end-to-end software platform for teams of scientists and pathologists to review, annotate and discuss cases, enabling rapid diagnostic consensus, quality control, and empowered discovery. Summary of Unpublished Results: The CBTTC/Blackfynn data platform enabled CBTTC members to engage in a cross-institutional collaboration to reach consensus on digital pathology data in ways that were previously not possible. We demonstrated that this solution removes existing barriers to collaborative efforts and provides a rich analytic and discovery platform bridging imaging with genomics and other data formats. The platform provides a new model for the scientific community to facilitate translation towards improved treatments for children diagnosed with brain tumors. Discussion and Future Direction: This pilot project will be scaled to other CBTTC sites for centralized review of pathology images to enable the research community to collaborative on specific projects. The next phase of platform development will include further integration CBTTC platforms fully integrating genomics data, and side-by-side viewing and analyses of MRI, pathology and clincal data to facilitate specific project work around large and complex research data types in a cloud environment. Citation Format: Amanda Christini, Angela J. Waanders, Joost B. Wagenaar, Alex S. Felmeister, Mariarita Santi, Nitin R. Wadhwani, Jennifer L. Mason, Mateusz P. Koptyra, Jena V. Lilly, Jeffrey W. Pennington, Rishi R. Lulla, Adam C. Resnick. Accelerating pediatric brain tumor research through team science solutions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2593. doi:10.1158/1538-7445.AM2017-2593

A parametrized approach for linear regression of interval data

Interval symbolic data is a complex data type that can often be obtained by summarizing large datasets. All existing linear regression approaches for interval data use certain fixed reference points to model intervals, such as midpoints, ranges and lower and upper bounds. This is a limitation, because different datasets might be better represented by different reference points. In this paper, we propose a new method for extracting knowledge from interval data. Our parametrized approach automatically extracts the best reference points from the regressor variables. These reference points are then used to build two linear regressions: one for the lower bounds of the response variable and another for its upper bounds. Before the regressions are applied, we compute a criterion to verify the mathematical coherence of predicted values. Mathematical coherence means that the upper bounds are greater than the lower bounds. If the criterion shows that the coherence is not guaranteed, we suggest the use of a novel interval Box-Cox transformation of the response variable. Experimental evaluations with synthetic and real interval datasets illustrate the advantages and the usefulness of the proposed method to perform interval linear regression.

An REA Ontology-Based Model for Mapping Big Data to Accounting Information Systems Elements

ABSTRACT The term “Big Data” refers to massive volumes of data that grow at an increasing rate and encompass complex data types such as audio and video. While the applications of Big Data and analytic techniques for business purposes have received considerable attention, it is less clear how external sources of Big Data relate to the transaction processing-oriented world of accounting information systems. This paper uses the Resource-Event-Agent Enterprise Ontology (REA) (McCarthy 1982; International Standards Organization [ISO] 2007) to model the implications of external Big Data sources on business transactions. The five-phase REA-based specification of a business transaction as defined in ISO (2007) is used to formally define associations between specific Big Data elements and business transactions. Using Big Data technologies such as Apache Hadoop and MapReduce, a number of information extraction patterns are specified for extracting business transaction-related information from Big Data. We also present a number of analytics patterns to demonstrate how decision making in accounting can benefit from integrating specific external Big Data sources and conventional transactional data. The model and techniques presented in this paper can be used by organizations to formalize the associations between external Big Data elements in their environment and their accounting information artifacts, to build architectures that extract information from external Big Data sources for use in an accounting context, and to leverage the power of analytics for more effective decision making.

Advanced methods for linking complex historical birth, death, marriage and census data

ABSTRACT
 ObjectiveRecent years have seen the development of novel techniques for linking complex types of data that contain records about different types of entities, for example bibliographic databases with records about authors, publications, and venues. Advanced approaches have been devised to link individuals and groups of records. These approaches exploit both the similarities between record attributes as well as the relationships between entities. Rather than linking records about different types of entities, in this work we study the novel problem of linking records where the same entity can have different roles and where these roles can change over time.
 ApproachWe specifically develop novel techniques for linking historical birth, death, marriage, and census certificates with the aim to reconstruct the population covered by these certificates over a period of time. Our techniques make use of constraints that consider roles, relationships, as well as time. Our first technique links certificates based on the specific roles of their individuals, and greedily selects pairs of certificates with the highest overall similarity while also considering 1-to-1 and 1-to-many linkage constraints. Our second hybrid technique combines graph, group, and temporal linkage, and also considers relationship information between individuals and groups. We compare these techniques with state-of-the-art group, collective, and graph-based linkage approaches.
 ResultsWe evaluate our proposed techniques on real Scottish data from 1861 to 1901 that cover the population of the Isle of Skye. In total, these data sets contain 119,042 certificates for 234,365 individuals. As ground truth we have a set of life-segments of records manually linked by domain experts. Our results indicate that even advanced techniques have difficulty in achieving high linkage quality compared to careful manual linkage. Two reasons for this are the very small name pool in our data and the changing nature of people's personal details over time. Both our proposed techniques, however, significantly outperform traditional pair-wise attribute similarity and group linkage approaches, with the greedy role-based technique achieving better results than the hybrid technique.
 ConclusionOur experiments on real data show that even with advanced linkage techniques that employ group, graph, relationship, and temporal approaches it is challenging to achieve high quality links from complex data such as birth, death, marriage and census certificates that span several decades. As future work we will improve all steps of our techniques with the goal of developing highly accurate, scalable, and automatic techniques for linking large-scale complex population databases.

Efficient Metric Indexing for Similarity Search and Similarity Joins

Spatial queries including similarity search and similarity joins are useful in many areas, such as multimedia retrieval, data integration, and so on. However, they are not supported well by commercial DBMSs. This may be due to the complex data types involved and the needs for flexible similarity criteria seen in real applications. In this paper, we propose a versatile and efficient disk-based index for metric data, the S pace-filling curve and Pivot-based B $^{+}$ -tree (SPB-tree). This index leverages the B $^+$ -tree, and uses space-filling curve to cluster data into compact regions, thus achieving storage efficiency. It utilizes a small set of so-called pivots to reduce significantly the number of distance computations when using the index. Further, it makes use of a separate random access file to support a broad range of data. By design, it is easy to integrate the SPB-tree into an existing DBMS. We present efficient algorithms for processing similarity search and similarity joins, as well as corresponding cost models based on SPB-trees. Extensive experiments using both real and synthetic data show that, compared with state-of-the-art competitors, the SPB-tree has much lower construction cost, smaller storage size, and supports more efficient similarity search and similarity joins with high accuracy cost models.

Welcome to Phytobiomes

In 2014, members of The American Phytopathological Society (APS) began to consider strategically the ways in which the accelerating flow of diverse types of complex data are fundamentally changing the ways in which we study, think about, and manage plant systems. Advancing technical capacities to generate plant, microbial and other organismal ‘omics’, environmental, and data produced at very fine spatial scales, coupled with expanding capabilities to integrate data across diverse scales of space and time, are providing novel insights into the networks of interaction that mediate plant productivity. In response to these enormous advances in technical capacities, APS created a Phytobiomes Initiative to chart a path forward. In 2015, APS brought together a diverse community of scientists in Washington DC for a strategically timed meeting to spearhead a new paradigm for crop improvement focusing on phytobiome-based approaches. The success of the human microbiome project paved the way, challenging us to push the boundaries to expand our knowledge of sustainable agriculture to meet the demands of feeding the growing global population. This meeting was instrumental in clarifying the vision of phytobiomes research, encapsulating systems-level understanding of diverse interacting components spanning multiple disciplines, species, and environments. To fully appreciate the breadth of scope of the Phytobiomes Initiative, we encourage you to review the Phytobiomes Roadmap ( http://www.phytobiomes.org/roadmap ), released in March 2016. The Phytobiomes journal was launched as an integral but independent part of the Phytobiomes Initiative. The Phytobiomes journal provides an international platform for fundamental, translational, and integrated research that accomplishes the overarching objective of offering a new vision for agriculture in which sustainable crop productivity is achieved through a systems-level understanding of the diverse interacting components of the phytobiome. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY Attribution 4.0 International license .

Exploratory querying of SPARQL endpoints in space and time

The linked data Web provides a simple and flexible way of accessing information resources in a self-descriptive format. This offers a realistic chance of perforating existing data silos. However, in order to do so, space, time and other semantic concepts need to function as dimensions for effectively exploring, querying and filtering contents. While triple stores, SPARQL endpoints, and RDF were designed for machine access, large burdens are still placed on a user to simultaneously explore and query the contents of a given endpoint according to these dimensions. First, one has to know the semantic concepts and the type of knowledge contained in an endpoint a-priori in order to query content effectively. Second, one has to be able to write and understand SPARQL and RDF. And third, one has to understand complex data type literals for space and time. In this article, we propose a way to deal with these challenges by interactive visual query construction, i.e., by letting query results feedback into both (space-time) exploration and filtering, and thus enabling exploratory querying. We propose design principles for SPEX (Spatio-temporal content explorer), a tool which helps people unfamiliar with the content of SPARQL endpoints or their syntax to explore the latter in space and time. In a preliminary user study on a repository of historical maps, we found that our feedback principles were effective, however, that successful question answering still requires improvements regarding space-time filtering, vocabulary explanation and the linking of space-time windows with other displays.

Differential privacy in the wild

Differential privacy has emerged as an important standard for privacy preserving computation over databases containing sensitive information about individuals. Research on differential privacy spanning a number of research areas, including theory, security, database, networks, machine learning, and statistics, over the last decade has resulted in a variety of privacy preserving algorithms for a number of analysis tasks. Despite maturing research efforts, the adoption of differential privacy by practitioners in industry, academia, or government agencies has so far been rare. Hence, in this tutorial, we will first describe the foundations of differentially private algorithm design that cover the state of the art in private computation on tabular data. In the second half of the tutorial we will highlight real world applications on complex data types, and identify research challenges in applying differential privacy to real world applications.

Turning Video Resource Management into Cloud Computing

Big data makes cloud computing more and more popular in various fields. Video resources are very useful and important to education, security monitoring, and so on. However, issues of their huge volumes, complex data types, inefficient processing performance, weak security, and long times for loading pose challenges in video resource management. The Hadoop Distributed File System (HDFS) is an open-source framework, which can provide cloud-based platforms and presents an opportunity for solving these problems. This paper presents video resource management architecture based on HDFS to provide a uniform framework and a five-layer model for standardizing the current various algorithms and applications. The architecture, basic model, and key algorithms are designed for turning video resources into a cloud computing environment. The design was tested by establishing a simulation system prototype.

Automatically learning shape specifications

This paper presents a novel automated procedure for discovering expressive shape specifications for sophisticated functional data structures. Our approach extracts potential shape predicates based on the definition of constructors of arbitrary user-defined inductive data types, and combines these predicates within an expressive first-order specification language using a lightweight data-driven learning procedure. Notably, this technique requires no programmer annotations, and is equipped with a type-based decision procedure to verify the correctness of discovered specifications. Experimental results indicate that our implementation is both efficient and effective, capable of automatically synthesizing sophisticated shape specifications over a range of complex data types, going well beyond the scope of existing solutions.

A Two-stage, Fitted Values Approach to Activity Matching

Accurate and rich representations of constituent actor populations are a critical component of agent-based models. Such populations are designed so that demographic, behavioral, procedural, and geographic characteristics of the synthetic population jointly reflect the available information about the true population. Information about the attributes to be mimicked in the synthetic population is often derived from survey samples of the real actors of interest -- such as firms in a market or households in a city. This additional information is then mapped to individual actors in such a way that each actor in the population represents one sample from the joint distribution of all assigned attributes. These actors then interact according to rules, which are functions of their attributes. Thus, accurate attribute matching is necessary to ensure that model outputs are meaningful. In real applications, behavioral surveys often yield complex data types, such as daily activity schedules or action sequences, for which it is difficult to conceive of adequate conditional models of behavior that could be used to generate new behavioral data as a function of covariates. Here the authors propose a method for assigning behavioral templates to synthetic agents from a set of survey templates. The method first maps the complex behavioral data to a reduced-dimension Euclidean space, then estimates conditional models in this space. The authors then make predictions in Euclidean space for synthetic actors and assign them the template schedule that minimizes the distance to the predicted value. By employing a two-step process, the authors also ensure that within-household dependence structures are maintained in the synthetic population. The authors illustrate the method with an application to a synthetic representation of households in the state of Israel and demonstrate superior ability to generate accurate joint distributions between demographic characteristics and behavioral activity relative to the standard behavior assignment method.

A swarm-trained k-nearest prototypes adaptive classifier with automatic feature selection for interval data

Some complex data types are capable of modeling data variability and imprecision. These data types are studied in the symbolic data analysis field. One such data type is interval data, which represents ranges of values and is more versatile than classic point data for many domains. This paper proposes a new prototype-based classifier for interval data, trained by a swarm optimization method. Our work has two main contributions: a swarm method which is capable of performing both automatic selection of features and pruning of unused prototypes and a generalized weighted squared Euclidean distance for interval data. By discarding unnecessary features and prototypes, the proposed algorithm deals with typical limitations of prototype-based methods, such as the problem of prototype initialization. The proposed distance is useful for learning classes in interval datasets with different shapes, sizes and structures. When compared to other prototype-based methods, the proposed method achieves lower error rates in both synthetic and real interval datasets.