Domain-specific languages for the design, deployment and manipulation of heterogeneous databases

The need for levels of availability and scalability beyond those supported by relational databases has led to the emergence of a new generation of purpose-specific databases grouped under the term NoSQL. In general, NoSQL databases are designed with horizontal scalability as a primary concern and deliver increased availability and fault tolerance at a cost of temporary inconsistency and reduced durability of data. To balance the requirements for data consistency and availability, organisations increasingly migrate towards hybrid data persistence architectures comprising both relational and NoSQL databases. The consensus is that this trend will only become stronger in the future; critical data will continue to be stored in ACID (largely relational) databases while non-critical data will be progressively migrated to high-availability NoSQL databases. Designing and deploying a hybrid data persistence architecture that involves a combination of relational and NoSQL databases is a complex, technically challenging and error-prone task. In this paper we outline a model-based methodology developed in the context of the EC-funded H2020 TYPHON project for designing, developing, querying and evolving such scalable architectures for persistence, analytics and monitoring of large volumes of hybrid (relational, graph-based, document-based, natural language, etc.) data, in a systematic and disciplined manner

Adaptive Management of Multimodel Data and Heterogeneous Workloads

Data management systems are facing a growing demand for a tighter integration of heterogeneous data from different applications and sources for both operational and analytical purposes in real-time. However, the vast diversification of the data management landscape has led to a situation where there is a trade-off between high operational performance and a tight integration of data. The difference between the growth of data volume and the growth of computational power demands a new approach for managing multimodel data and handling heterogeneous workloads. With PolyDBMS we present a novel class of database management systems, bridging the gap between multimodel database and polystore systems. This new kind of database system combines the operational capabilities of traditional database systems with the flexibility of polystore systems. This includes support for data modifications, transactions, and schema changes at runtime. With native support for multiple data models and query languages, a PolyDBMS presents a holistic solution for the management of heterogeneous data. This does not only enable a tight integration of data across different applications, it also allows a more efficient usage of resources. By leveraging and combining highly optimized database systems as storage and execution engines, this novel class of database system takes advantage of decades of database systems research and development. In this thesis, we present the conceptual foundations and models for building a PolyDBMS. This includes a holistic model for maintaining and querying multiple data models in one logical schema that enables cross-model queries. With the PolyAlgebra, we present a solution for representing queries based on one or multiple data models while preserving their semantics. Furthermore, we introduce a concept for the adaptive planning and decomposition of queries across heterogeneous database systems with different capabilities and features. The conceptual contributions presented in this thesis materialize in Polypheny-DB, the first implementation of a PolyDBMS. Supporting the relational, document, and labeled property graph data model, Polypheny-DB is a suitable solution for structured, semi-structured, and unstructured data. This is complemented by an extensive type system that includes support for binary large objects. With support for multiple query languages, industry standard query interfaces, and a rich set of domain-specific data stores and data sources, Polypheny-DB offers a flexibility unmatched by existing data management solutions

Quarry: A user-centered big data integration platform

Obtaining valuable insights and actionable knowledge from data requires cross-analysis of domain data typically coming from various sources. Doing so, inevitably imposes burdensome processes of unifying different data formats, discovering integration paths, and all this given specific analytical needs of a data analyst. Along with large volumes of data, the variety of formats, data models, and semantics drastically contribute to the complexity of such processes. Although there have been many attempts to automate various processes along the Big Data pipeline, no unified platforms accessible by users without technical skills (like statisticians or business analysts) have been proposed. In this paper, we present a Big Data integration platform (Quarry) that uses hypergraph-based metadata to facilitate (and largely automate) the integration of domain data coming from a variety of sources, and provides an intuitive interface to assist end users both in: (1) data exploration with the goal of discovering potentially relevant analysis facets, and (2) consolidation and deployment of data flows which integrate the data, and prepare them for further analysis (descriptive or predictive), visualization, and/or publishing. We validate Quarry’s functionalities with the use case of World Health Organization (WHO) epidemiologists and data analysts in their fight against Neglected Tropical Diseases (NTDs).This work is partially supported by GENESIS project, funded by the Spanish Ministerio de Ciencia, Innovación y Universidades under project TIN2016-79269-R.Peer ReviewedPostprint (author's final draft

Processing Analytical Queries in the AWESOME Polystore [Information Systems Architectures]

Modern big data applications usually involve heterogeneous data sources and analytical functions, leading to increasing demand for polystore systems, especially analytical polystore systems. This paper presents AWESOME system along with a domain-specific language ADIL. ADIL is a powerful language which supports 1) native heterogeneous data models such as Corpus, Graph, and Relation; 2) a rich set of analytical functions; and 3) clear and rigorous semantics. AWESOME is an efficient tri-store middle-ware which 1) is built on the top of three heterogeneous DBMSs (Postgres, Solr, and Neo4j) and is easy to be extended to incorporate other systems; 2) supports the in-memory query engines and is equipped with analytical capability; 3) applies a cost model to efficiently execute workloads written in ADIL; 4) fully exploits machine resources to improve scalability. A set of experiments on real workloads demonstrate the capability, efficiency, and scalability of AWESOME

Mixed-instance querying: a lightweight integration architecture for data journalism

International audienceAs the world's affairs get increasingly more digital, timely production and consumption of news require to efficiently and quickly exploit heterogeneous data sources. Discussions with journalists revealed that content management tools currently at their disposal fall very short of expectations. We demonstrate TATOOINE, a lightweight data integration prototype, which allows to quickly set up integration queries across (very) heterogeneous data sources, capitalizing on the many data links (joins) available in this application domain. Our demonstration is based on scenarios we study in collaboration with Le Monde, France's major newspaper