NOSQL design for analytical workloads: Variability matters

Big Data has recently gained popularity and has strongly questioned relational databases as universal storage systems, especially in the presence of analytical workloads. As result, co-relational alternatives, commonly known as NOSQL (Not Only SQL) databases, are extensively used for Big Data. As the primary focus of NOSQL is on performance, NOSQL databases are directly designed at the physical level, and consequently the resulting schema is tailored to the dataset and access patterns of the problem in hand. However, we believe that NOSQL design can also benefit from traditional design approaches. In this paper we present a method to design databases for analytical workloads. Starting from the conceptual model and adopting the classical 3-phase design used for relational databases, we propose a novel design method considering the new features brought by NOSQL and encompassing relational and co-relational design altogether.Peer ReviewedPostprint (author's final draft

Design of petroleum company's metadata and an effective knowledge mapping methodology

Success of information flow depends on intelligent datastorage and its management in a multi-disciplinaryenvironment. Multi-dimensional data entities, data typesand ambiguous semantics, often pose uncertainty andinconsistency in data retrieval from volumes of petroleumdata sources. In our approach, conceptual schemas andsub-schemas have been described based on variousoperational functions of the petroleum industry. Theseschemas are integrated, to ensure their consistency andvalidity, so that the information retrieved from anintegrated metadata (in the form of a data warehouse)structure derives its authenticity from its implementation.The data integration process validating the petroleummetadata has been demonstrated for one of the Gulfoffshore basins for an effective knowledge mapping andinterpreting it successfully for the derivation of usefulgeological knowledge. Warehoused data are used formining data patterns, trends and correlations amongknowledge-base data attributes that led to interpretation ofinteresting geological features. These technologies appearto be more amenable for exploration of more petroleumresources in the mature gulf basins

Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals

A Survey on Mapping Semi-Structured Data and Graph Data to Relational Data

The data produced by various services should be stored and managed in an appropriate format for gaining valuable knowledge conveniently. This leads to the emergence of various data models, including relational, semi-structured, and graph models, and so on. Considering the fact that the mature relational databases established on relational data models are still predominant in today's market, it has fueled interest in storing and processing semi-structured data and graph data in relational databases so that mature and powerful relational databases' capabilities can all be applied to these various data. In this survey, we review existing methods on mapping semi-structured data and graph data into relational tables, analyze their major features, and give a detailed classification of those methods. We also summarize the merits and demerits of each method, introduce open research challenges, and present future research directions. With this comprehensive investigation of existing methods and open problems, we hope this survey can motivate new mapping approaches through drawing lessons from eachmodel's mapping strategies, aswell as a newresearch topic - mapping multi-model data into relational tables.Peer reviewe

Conditions for interoperability

Interoperability for information systems remains a challenge both at the semantic and organisational levels. The original three-level architecture for local databases needs to be replaced by a categorical four-level one based on concepts, constructions, schema types and data together with the mappings between them. Such an architecture provides natural closure as further levels are superfluous even in a global environment. The architecture is traversed by means of the Godement calculus: arrows may be composed at any level as well as across levles. The necessary and sufficient conditions for interoperability are satisfied by composable (formal) diagrams both for intension and extension in categories that are cartesian closed and locally cartesian closed. Methods like partial categories and sketches in schema design can benefit from Freyd’s punctured diagrams to identify precisely type-forcing natural transformations. Closure is better achieved in standard full categories. Global interoperability of extension can be achieved through semantic annotation but only if applied at run time

Big Data Guided Resources Businesses – Leveraging Location Analytics and Managing Geospatial-temporal Knowledge

Location data rapidly grow with fast-changing logistics and business rules. Due to fast-growing business ventures and their diverse operations locally and globally, location-based information systems are in demand in resource industries. Data sources in these industries are spatial-temporal, with petabytes in size. Managing volumes and various data in periodic and geographic dimensions using the existing modelling methods is challenging. The current relational database models have implementation challenges, including the interpretation of data views. Multidimensional models are articulated to integrate resource databases with spatial-temporal attribute dimensions. Location and periodic attribute dimensions are incorporated into various schemas to minimise ambiguity during database operations, ensuring resource data's uniqueness and monotonic characteristics. We develop an integrated framework compatible with the multidimensional repository and implement its metadata in resource industries. The resources’ metadata with spatial-temporal attributes enables business research analysts a scope for data views’ interpretation in new geospatial knowledge domains for financial decision support