A Model-Driven Approach to Automate Data Visualization in Big Data Analytics

In big data analytics, advanced analytic techniques operate on big data sets aimed at complementing the role of traditional OLAP for decision making. To enable companies to take benefit of these techniques despite the lack of in-house technical skills, the H2020 TOREADOR Project adopts a model-driven architecture for streamlining analysis processes, from data preparation to their visualization. In this paper we propose a new approach named SkyViz focused on the visualization area, in particular on (i) how to specify the user's objectives and describe the dataset to be visualized, (ii) how to translate this specification into a platform-independent visualization type, and (iii) how to concretely implement this visualization type on the target execution platform. To support step (i) we define a visualization context based on seven prioritizable coordinates for assessing the user's objectives and conceptually describing the data to be visualized. To automate step (ii) we propose a skyline-based technique that translates a visualization context into a set of most-suitable visualization types. Finally, to automate step (iii) we propose a skyline-based technique that, with reference to a specific platform, finds the best bindings between the columns of the dataset and the graphical coordinates used by the visualization type chosen by the user. SkyViz can be transparently extended to include more visualization types on the one hand, more visualization coordinates on the other. The paper is completed by an evaluation of SkyViz based on a case study excerpted from the pilot applications of the TOREADOR Project

Prevalence of Golden retriever in European dogs with lymphoma: preliminary data

Introduction. Canine breeds, being genetic clusters, are good models for studies on genetic predisposition. Golden retriever (GR) has been described with a high incidence of both lymphoma overall (19%) and T zone lymphoma (TZL, 40%) with differences in different geographical areas in US. This breed predisposition is confirmed in Japanese but not in European (EU) case series although specific studies are still lacking.Aim of the present study is to investigate the prevalence of GR in a huge case series of canine lymphomas from different EU countries and to compare prevalence of different subtypes with studies in extra-EU countries, in order to support a possible different genetic predisposition.Materials and methods. Signalment data on 1734 consecutive cases of canine lymphoma collected from 9 different European countries are retrospectively analysed. When subtypes are available, cases are furtherly separated in three subtype groups: 1) B-cell lymphoma, 2) T-cell lymphoma-high grade, 3) TZL. Odds ratio (OR) for different lymphoma subtypes are calculated in comparison with mixed breed population, considered as control.Results. Overall prevalence of GR is 5.19% (range 1.59-7.32%) of lymphoma cases and differs from that reported in American and Japanese caseloads. Prevalence slightly varies among EU countries and no subtypes predilection is found if compared with mixed breed. Concerning Italian cohort, GR is not predisposed to develop a lymphoma when normalized for the breed prevalence (OR=1.49, 95% confidence interval=0.87-2.55, p=0.14).Discussion. Prevalence of lymphoma in EU population of GR is much lower than that of US. No predisposition is identified in EU GR for TZL differently from US and Japan. Being genetic of European GR population quite different from American and Japanese ones this suggest a possible different genetic predisposition. Slight differences in GR lymphoma prevalence among European countries likely reflects different breed distribution rather than different genetic predisposition

Describing and Assessing Cubes Through Intentional Analytics

The Intentional Analytics Model (IAM) has been envisioned as a way to tightly couple OLAP and analytics by (i) letting users explore multidimensional cubes stating their intentions, and (ii) returning multidimensional data coupled with knowledge insights in the form of annotations of subsets of data. Goal of this demonstration is to showcase the IAM approach using a notebook where the user can create a data exploration session by writing describe and assess statements, whose results are displayed by combining tabular data and charts so as to bring the highlights discovered to the user's attention. The demonstration plan will show the effectiveness of the IAM approach in supporting data exploration and analysis and its added value as compared to a traditional OLAP session by proposing two scenarios with guided interaction and letting users run custom sessions

Schema Profiling of Document Stores

In document stores, schema is a soft concept and the documents in a collection can have different schemata; this gives designers and implementers augmented flexibility but requires an extra effort to understand the rules that drove the use of alternative schemata when heterogeneous documents are to be analyzed or integrated. In this paper we outline a technique, called schema profiling, to explain the schema variants within a collection in document stores by capturing the hidden rules explaining the use of these variants; we express these rules in the form of a decision tree, called schema profile, whose main feature is the coexistence of value-based and schema-based conditions. Consistently with the requirements we elicited from real users, we aim at creating explicative, precise, and concise schema profiles; to quantitatively assess these qualities we introduce a novel measure of entropy

Towards a Multi-Model Approach to Support User-Driven Extensibility in Data Warehouses: Agro-ecology Case Study

Information systems have evolved into complex data platforms supporting end-to-end data-intensive needs, aimed at coping with the different V's that characterize Big Data. In particular, multi-model databases (MMDBs) have been proposed to natively support storing and querying data in different (schemaless) models, so as to better handle Variety. In this work we envision a new data warehouse architecture in which an MMDB is used to enable on-the-fly user-driven extensions of multidimensional cubes with additional data, while ensuring support to variable and complex data and keeping the impact on ETL low. After proposing the architecture with the aid of a case study on the management of emerging plant disease, we discuss the main associated open issues

EXODuS: Exploratory OLAP over Document Stores

OLAP has been extensively used for a couple of decades as a data analysis approach to support decision making on enterprise structured data. Now, with the wide diffusion of NoSQL databases holding semi-structured data, there is a growing need for enabling OLAP on document stores as well, to allow non-expert users to get new insights and make better decisions. Unfortunately, due to their schemaless nature, document stores are hardly accessible via direct OLAP querying. In this paper we propose EXODuS, an interactive, schema-on-read approach to enable OLAP querying of document stores in the context of self-service BI and exploratory OLAP. To discover multidimensional hierarchies in document stores we adopt a data-driven approach based on the mining of approximate functional dependencies; to ensure good performances, we incrementally build local portions of hierarchies for the levels involved in the current user query. Users execute an analysis session by expressing well-formed multidimensional queries related by OLAP operations; these queries are then translated into the native query language of MongoDB, one of the most popular document-based DBMS. An experimental evaluation on real-world datasets shows the efficiency of our approach and its compatibility with a real-time setting

Visualization Requirements for Business Intelligence Analytics: A Goal-Based, Iterative Framework

Information visualization plays a key role in business intelligence analytics. With ever larger amounts of data that need to be interpreted, using the right visualizations is crucial in order to understand the underlying patterns and results obtained by analysis algorithms. Despite its importance, defining the right visualization is still a challenging task. Business users are rarely experts in information visualization, and they may not exactly know the most adequate visualization tools or patterns for their goals. Consequently, misinterpreted graphs and wrong results can be obtained, leading to missed opportunities and significant losses for companies. The main problem underneath is a lack of tools and methodologies that allow non-expert users to define their visualization and data analysis goals in business terms. In order to tackle this problem, we present an iterative goal-oriented approach based on the i* language for the automatic derivation of data visualizations. Our approach links non-expert user requirements to the data to be analyzed, choosing the most suited visualization techniques in a semi-automatic way. The great advantage of our proposal is that we provide non-expert users with the best suited visualizations according to their information needs and their data with little effort and without requiring expertise in information visualization

Luminal endothelialization of small caliber silk tubular graft for vascular constructs engineering

: The constantly increasing incidence of coronary artery disease worldwide makes necessary to set advanced therapies and tools such as tissue engineered vessel grafts (TEVGs) to surpass the autologous grafts [(i.e., mammary and internal thoracic arteries, saphenous vein (SV)] currently employed in coronary artery and vascular surgery. To this aim, in vitro cellularization of artificial tubular scaffolds still holds a good potential to overcome the unresolved problem of vessel conduits availability and the issues resulting from thrombosis, intima hyperplasia and matrix remodeling, occurring in autologous grafts especially with small caliber (<6 mm). The employment of silk-based tubular scaffolds has been proposed as a promising approach to engineer small caliber cellularized vascular constructs. The advantage of the silk material is the excellent manufacturability and the easiness of fiber deposition, mechanical properties, low immunogenicity and the extremely high in vivo biocompatibility. In the present work, we propose a method to optimize coverage of the luminal surface of silk electrospun tubular scaffold with endothelial cells. Our strategy is based on seeding endothelial cells (ECs) on the luminal surface of the scaffolds using a low-speed rolling. We show that this procedure allows the formation of a nearly complete EC monolayer suitable for flow-dependent studies and vascular maturation, as a step toward derivation of complete vascular constructs for transplantation and disease modeling