Relational database design is a well-understood process enabled by a combination of database theory (e.g., normal forms) as well as conceptual modeling (e.g., ER-based design). In contrast, database design for NoSQL databases, notably document databases, is often approached in a much more ad hoc manner. It is frequently driven by application details and physical considerations that muddy the design process in ways all too reminiscent of the pre-relational database era. In this paper, we argue for a return to sanity - for a logical, data-first, conceptually grounded approach to document database design. We explain how such an approach can work, yielding a clean, query-friendly document database design. We also highlight a collection of document (JSON) anti-patterns to avoid. The process and the anti-patterns both stem from the authors' experiences in current and past lives when dealing with a wide variety of JSON document data from commercial applications, government applications, and university research applications.

Couchbase Capella is a scalable document-oriented database service in the cloud. Its existing Capella Operational service is based on a shared-nothing architecture and supports high volumes of low-latency queries and updates for JSON documents. Its new Capella Columnar cloud service complements the Operational service. The Capella Columnar service supports complex analytical queries (e.g., ad hoc joins and aggregations) over large collections of JSON documents that can originate from a variety of Couchbase and non-Couchbase data sources and formats and can either be stored and managed by the Capella Columnar service or externally stored and accessed on demand at query time. This paper describes the new Capella Columnar service, looking both over and under the hood.

This paper presents Sentaur, a middleware designed, built, and deployed to support sensor-based smart space analytical applications. Sentaur supports a powerful data model that decouples semantic data (about the application domain) from sensor data (using which the semantic data is derived). By supporting mechanisms to map/translate data, concepts, and queries between the two levels, Sentaur relieves application developers from having to know or reason about either capabilities of sensors or write sensor specific code. This paper describes Sentaur's data model, its translation strategy, and highlights its benefits through real-world case studies.

Emerging domains, such as sensor-driven smart spaces and social media analytics, require incoming data to be enriched prior to its use. Enrichment often consists of machine learning (ML) functions that are too expensive/infeasible to execute at ingestion. We develop a strategy entitled Just-in-time ENrichmeNt in quERy Processing (JENNER) to support interactive analytics over data as soon as it arrives for such application context. JENNER exploits the inherent tradeoffs of cost and quality often displayed by the ML functions to progressively improve query answers during query execution. We describe how JENNER works for a large class of SPJ and aggregation queries that form the bulk of data analytics workload. Our experimental results on real datasets (IoT and Tweet) show that JENNER achieves progressive answers performing significantly better than the naive strategies of achieving progressive computation.

Despite exciting progress on cryptography, secure and efficient query processing over outsourced data remains an open challenge. We develop a communication-efficient and information-theoretically secure system, entitledObscurefor aggregation queries with conjunctive or disjunctive predicates, using secret-sharing.Obscureis strongly secure (i.e., secure regardless of the computational-capabilities of an adversary) and prevents the network, as well as, the (adversarial) servers to learn the user’s queries, results, or the database. In addition,Obscureprovides additional security features, such as hiding access-patterns (i.e., hiding the identity of the tuple satisfying a query) and hiding query-patterns (i.e., hiding which two queries are identical). Also,Obscuredoes not require any communication between any two servers that store the secret-shared data before/during/after the query execution. Moreover, our techniques deal with the secret-shared data that is outsourced by a single or multiple database owners, as well as, allows a user, which may not be the database owner, to execute the query over secret-shared data. We further develop (non-mandatory) privacy-preserving result verification algorithms that detect malicious behaviors, and experimentally validate the efficiency ofObscureon large datasets, the size of which prior approaches of secret-sharing or multi-party computation systems have not scaled to.

This paper proposes Prism, a secret sharing based approach to compute private set operations (i.e., intersection and union), as well as aggregates over outsourced databases belonging to multiple owners. Prism enables data owners to pre-load the data onto non-colluding servers and exploits the additive and multiplicative properties of secret-shares to compute the above-listed operations in (at most) two rounds of communication between the servers (storing the secret-shares) and the querier, resulting in a very efficient implementation. Also, Prism does not require communication among the servers and supports result verification techniques for each operation to detect malicious adversaries. Experimental results show that Prism scales both in terms of the number of data owners and database sizes, to which prior approaches do not scale.

Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. We develop communication-efficient and information-theoretically secure algorithms for privacy-preserving aggregation queries using multi-party computation (MPC). Specifically, query processing techniques over secret-shared data outsourced by single or multiple database owners are developed. These algorithms allow a user to execute queries on the secret-shared database and also prevent the network and the (adversarial) clouds to learn the user's queries, results, or the database. We further develop (non-mandatory) privacy-preserving result verification algorithms that detect malicious behaviors, and experimentally validate the efficiency of our approach over large datasets, the size of which prior approaches to secret-sharing or MPC systems have not scaled to.