Recent advances in natural language understanding and processing have resulted in renewed interest in natural language interfaces to data, which provide an easy mechanism for non-technical users to access and query the data. While early systems evolved from keyword search and focused on simple factual queries, the complexity of both the input sentences as well as the generated SQL queries has evolved over time. More recently, there has also been a lot of focus on using conversational interfaces for data analytics, empowering a line of business owners and non-technical users with quick insights into the data. There are three main challenges in natural language querying: (1) identifying the entities involved in the user utterance, (2) connecting the different entities in a meaningful way over the underlying data source to interpret user intents, and finally (3) generating a structured query in the form of SQL or SPARQL. There are two main approaches in the literature for interpreting a user’s natural language query. Rule-based systems make use of semantic indices, ontologies, and knowledge graphs to identify the entities in the query, understand the intended relationships between those entities, and utilize grammars to generate the target queries. With the advances in deep learning-based language models, there have been many text-to-SQL approaches that try to interpret the query holistically using deep learning models. Hybrid approaches that utilize both rule-based techniques as well as deep learning models are also emerging by combining the strengths of both approaches. Conversational interfaces are the next natural step to one-shot natural language querying by exploiting query context between multiple turns of conversation for disambiguation. In this monograph, we review the background technologies that are used in natural language interfaces, and survey the different approaches to natural language querying. We also describe conversational interfaces for data analytics and discuss several benchmarks used for natural language querying research and evaluation.

Equipping machines with comprehensive knowledge of the world’s entities and their relationships has been a longstanding goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canon-icalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.

Data provenance has evolved from a niche topic to a mainstream area of research in databases and other research communities. This article gives a comprehensive introduction to data provenance. The main focus is on provenance in the context of databases. However, it will be insightful to also consider connections to related research in programming languages, software engineering, semantic web, formal logic, and other communities. The target audience are researchers and practitioners that want to gain a solid understanding of data provenance and the state-of-the-art in this research area. The article only assumes that the reader has a basic understanding of database concepts, but not necessarily any prior exposure to provenance.

This article provides an overview of recent developments in mainmemory database systems. With growing memory sizes and memory prices dropping by a factor of 10 every 5 years, data having a “primary home” in memory is now a reality. Main-memory databases eschew many of the traditional architectural pillars of relational database systems that optimized for disk-resident data. The result of these memory-optimized designs are systems that feature several innovative approaches to fundamental issues (e.g., concurrency control, query processing) that achieve orders of magnitude performance improvements over traditional designs. Our survey covers five main issues and architectural choices that need to be made when building a high performance main-memory optimized database: data organization and storage, indexing, concurrency control, durability and recovery techniques, and query processing and compilation. We focus our survey on four commercial and research systems: H-Store/VoltDB, Hekaton, HyPer, and SAP HANA. These systems are diverse in their design choices and form a representative sample of the state of the art in main-memory database systems. We also cover other commercial and academic systems, along with current and future research trends.

Crowdsourcing and human computation enable organizations to accomplish tasks that are currently not possible for fully automated techniques to complete, or require more flexibility and scalability than traditional employment relationships can facilitate. In the area of data processing, companies have benefited from crowd workers on platforms such as Amazon’s Mechanical Turk or Upwork to complete tasks as varied as content moderation, web content extraction, entity resolution, and video/audio/image processing. Several academic researchers from diverse areas ranging from the social sciences to computer science have embraced crowdsourcing as a research area, resulting in algorithms and systems that improve crowd work quality, latency, or cost. Given the relative nascence of the field, the academic and the practitioner communities have largely operated independently of each other for the past decade, rarely exchanging techniques and experiences. In this book, we aim to narrow the gap between academics and practitioners. On the academic side, we summarize the state of the art in crowd-powered algorithms and system design tailored to large-scale data processing. On the industry side, we survey 13 industry users (e.g., Google, Facebook, Microsoft) and 4 marketplace providers of crowd work (e.g., CrowdFlower, Upwork) to identify how hundreds of engineers and tens of million dollars are invested in various crowdsourcing solutions. Through the book, we hope to simultaneously introduce academics to real problems that practitioners encounter every day, and provide a survey of the state of the art for practitioners to incorporate into their designs. Through our surveys, we also highlight the fact that crowd-powered data processing is a large and growing field. Over the next decade, we believe that most technical organizations will in some way benefit from crowd work, and hope that this book can help guide the effective adoption of crowdsourcing across these organizations.

Methods for Approximate Query Processing (AQP) are essential for dealing with massive data. They are often the only means of providing interactive response times when exploring massive datasets, and are also needed to handle high speed data streams. These methods proceed by computing a lossy, compact synopsis of the data, and then executing the query of interest against the synopsis rather than the entire dataset. We describe basic principles and recent developments in AQP. We focus on four key synopses: random samples, histograms, wavelets, and sketches. We consider issues such as accuracy, space and time efficiency, optimality, practicality, range of applicability, error bounds on query answers, and incremental maintenance. We also discuss the trade-offs between the different synopsis types.

As organizations depend on, possibly distributed, information systems for operational, decisional and strategic activities, they are vulnerable to security breaches leading to data theft and unauthorized disclosures even as they gain productivity and efficiency advantages. Though several techniques, such as encryption and digital signatures, are available to protect data when transmitted across sites, a truly comprehensive approach for data protection must include mechanisms for enforcing access control policies based on data contents, subject qualifications and characteristics, and other relevant contextual information, such as time. It is well understood today that the semantics of data must be taken into account in order to specify effective access control policies. To address such requirements, over the years the database security research community has developed a number of access control techniques and mechanisms that are specific to database systems. In this monograph, we present a comprehensive state of the art about models, systems and approaches proposed for specifying and enforcing access control policies in database management systems. In addition to surveying the foundational work in the area of access control for database systems, we present extensive case studies covering advanced features of current database management systems, such as the support for fine-grained and context-based access control, the support for mandatory access control, and approaches for protecting the data from insider threats. The monograph also covers novel approaches, based on cryptographic techniques, to enforce access control and surveys access control models for object-databases and XML data. For the reader not familiar with basic notions concerning access control and cryptography, we include a tutorial presentation on these notions. Finally, the monograph concludes with a discussion on current challenges for database access control and security, and preliminary approaches addressing some of these challenges.

Database Management Systems (DBMSs) are a ubiquitous and critical component of modern computing, and the result of decades of research and development in both academia and industry. Historically, DBMSs were among the earliest multi-user server systems to be developed, and thus pioneered many systems design techniques for scalability and reliability now in use in many other contexts. While many of the algorithms and abstractions used by a DBMS are textbook material, there has been relatively sparse coverage in the literature of the systems design issues that make a DBMS work. This paper presents an architectural discussion of DBMS design principles, including process models, parallel architecture, storage system design, transaction system implementation, query processor and optimizer architectures, and typical shared components and utilities. Successful commercial and open-source systems are used as points of reference, particularly when multiple alternative designs have been adopted by different groups.

Different notions of provenance for database queries have been proposed and studied in the past few years. In this article, we detail three main notions of database provenance, some of their applications, and compare and contrast amongst them. Specifically, we review why, how, and where provenance, describe the relationships among these notions of provenance, and describe some of their applications in confidence computation, view maintenance and update, debugging, and annotation propagation.