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

To have an Idea on NoSQL Databases

NoSQL databases (initially non-SQL, then Not Only SQL) are specifically designed to handle large amounts of data. They have been developed since the 1970s, but they have gained the interest of academia and industry for about two decades. This is because of their powerful characteristics and lack of relational databases, which are the most widely used data sources around the world. Indeed, these databases are based on the relational model, which is materialized by a relational database management system (RDBMS). Although RDBMS efficiently manage data (tables), they have many drawbacks that make them unsuitable for managing current data, which come mainly from Internet applications. They are called Big Data and they are used for example by Twitter, FaceBook, LinkedIn, .... They are very numerous and tend to change quickly. In fact, among the disadvantages of relational databases, we can mention: non-flexibility, non-scalability, ... On the contrary, NoSQL databases evolve very well (scaling) and almost all NoSQL databases are schema-free (we can add or delete an entity or a relationship at any time during execution). In this article, we begin by giving an overview of relational databases and their characteristics. We then describe the NoSQL databases and their main characteristics, knowing that there are as many different characteristics as  "NoSQL databases" products. We then give the taxonomy of NoSQL databases, which distinguishes four main types of NoSQL databases: key-value, wide-column, document and graphical databases. We will then give some elements of each type of database through the use of a product, an implementation of a kind of such a database

Business Intelligence on Non-Conventional Data

The revolution in digital communications witnessed over the last decade had a significant impact on the world of Business Intelligence (BI). In the big data era, the amount and diversity of data that can be collected and analyzed for the decision-making process transcends the restricted and structured set of internal data that BI systems are conventionally limited to. This thesis investigates the unique challenges imposed by three specific categories of non-conventional data: social data, linked data and schemaless data. Social data comprises the user-generated contents published through websites and social media, which can provide a fresh and timely perception about people’s tastes and opinions. In Social BI (SBI), the analysis focuses on topics, meant as specific concepts of interest within the subject area. In this context, this thesis proposes meta-star, an alternative strategy to the traditional star-schema for modeling hierarchies of topics to enable OLAP analyses. The thesis also presents an architectural framework of a real SBI project and a cross-disciplinary benchmark for SBI. Linked data employ the Resource Description Framework (RDF) to provide a public network of interlinked, structured, cross-domain knowledge. In this context, this thesis proposes an interactive and collaborative approach to build aggregation hierarchies from linked data. Schemaless data refers to the storage of data in NoSQL databases that do not force a predefined schema, but let database instances embed their own local schemata. In this context, this thesis proposes an approach to determine the schema profile of a document-based database; the goal is to facilitate users in a schema-on-read analysis process by understanding the rules that drove the usage of the different schemata. A final and complementary contribution of this thesis is an innovative technique in the field of recommendation systems to overcome user disorientation in the analysis of a large and heterogeneous wealth of data

Proceedings TLAD 2012:10th International Workshop on the Teaching, Learning and Assessment of Databases

This is the tenth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2012). TLAD 2012 is held on the 9th July at the University of Hertfordshire and hopes to be just as successful as its predecessors. The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics and teachers from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers. Due to the healthy number of high quality submissions this year, the workshop will present eight peer reviewed papers. Of these, six will be presented as full papers and two as short papers. These papers cover a number of themes, including: the teaching of data mining and data warehousing, SQL and NoSQL, databases at school, and database curricula themselves. The final paper will give a timely ten-year review of TLAD workshops, and it is expected that these papers will lead to a stimulating closing discussion, which will continue beyond the workshop. We also look forward to a keynote presentation by Karen Fraser, who has contributed to many TLAD workshops as the HEA organizer. Titled “An Effective Higher Education Academy”, the keynote will discuss the Academy’s plans for the future and outline how participants can get involved

Proceedings TLAD 2012:10th International Workshop on the Teaching, Learning and Assessment of Databases

This is the tenth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2012). TLAD 2012 is held on the 9th July at the University of Hertfordshire and hopes to be just as successful as its predecessors. The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics and teachers from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers. Due to the healthy number of high quality submissions this year, the workshop will present eight peer reviewed papers. Of these, six will be presented as full papers and two as short papers. These papers cover a number of themes, including: the teaching of data mining and data warehousing, SQL and NoSQL, databases at school, and database curricula themselves. The final paper will give a timely ten-year review of TLAD workshops, and it is expected that these papers will lead to a stimulating closing discussion, which will continue beyond the workshop. We also look forward to a keynote presentation by Karen Fraser, who has contributed to many TLAD workshops as the HEA organizer. Titled “An Effective Higher Education Academy”, the keynote will discuss the Academy’s plans for the future and outline how participants can get involved

The future of social is personal: the potential of the personal data store

This chapter argues that technical architectures that facilitate the longitudinal, decentralised and individual-centric personal collection and curation of data will be an important, but partial, response to the pressing problem of the autonomy of the data subject, and the asymmetry of power between the subject and large scale service providers/data consumers. Towards framing the scope and role of such Personal Data Stores (PDSes), the legalistic notion of personal data is examined, and it is argued that a more inclusive, intuitive notion expresses more accurately what individuals require in order to preserve their autonomy in a data-driven world of large aggregators. Six challenges towards realising the PDS vision are set out: the requirement to store data for long periods; the difficulties of managing data for individuals; the need to reconsider the regulatory basis for third-party access to data; the need to comply with international data handling standards; the need to integrate privacy-enhancing technologies; and the need to future-proof data gathering against the evolution of social norms. The open experimental PDS platform INDX is introduced and described, as a means of beginning to address at least some of these six challenges

Automated database design for document stores with multicriteria optimization

Document stores have gained popularity among NoSQL systems mainly due to the semi-structured data storage structure and the enhanced query capabilities. The database design in document stores expands beyond the first normal form by encouraging de-normalization through nesting. This hinders the process, as the number of alternatives grows exponentially with multiple choices in nesting (including different levels) and referencing (including the direction of the reference). Due to this complexity, document store data design is mostly carried out in trial-and-error or ad-hoc rule-based approaches. However, the choices affect multiple, often conflicting, aspects such as query performance, storage space, and complexity of the documents. To overcome these issues, in this paper, we apply multicriteria optimization. Our approach is driven by a query workload and a set of optimization objectives. First, we formalize a canonical model to represent alternative designs and introduce an algebra of transformations that can systematically modify a design. Then, using these transformations, we implement a local search algorithm driven by a loss function that can propose near-optimal designs with high probability. Finally, we compare our prototype against an existing document store data design solution purely driven by query cost, where our proposed designs have better performance and are more compact with less redundancy.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has been funded by the European Commission through the Erasmus Mundus Joint Doctorate "Information Technologies for Business Intelligence—Doctoral College" (IT4BI-DC). Sergi Nadal is partly supported by the Spanish Ministerio de Ciencia e Innovación, as well as the European Union—NextGenerationEU, under project FJC2020-045809-I / AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

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