Multi-model Data Management : What's New and What's Next?

TutorialAs more businesses realized that data, in all forms and sizes, is critical to making the best possible decisions, we see the continued growth of systems that support massive volume of non-relational or unstructured forms of data. Nothing shows the picture more starkly than the Gartner Magic quadrant for operational database management systems, which assumes that, by 2017, all leading operational DBMSs will offer multiple data models, relational and NoSQL, in a single DBMS platform. Having a single data platform for managing both well-structured data and NoSQL data is beneficial to users; this approach reduces significantly integration, migration, development, maintenance, and operational issues. Therefore, a challenging research work is how to develop efficient consolidated single data management platform covering both relational data and NoSQL to reduce integration issues, simplify operations, and eliminate migration issues. In this tutorial, we review the previous work on multi-model data management and provide the insights on the research challenges and directions for future work. The slides and more materials of this tutorial can be found at http://udbms.cs.helsinki.fi/?tutorials/edbt2017.Peer reviewe

A Nine Month Progress Report on an Investigation into Mechanisms for Improving Triple Store Performance

This report considers the requirement for fast, efficient, and scalable triple stores as part of the effort to produce the Semantic Web. It summarises relevant information in the major background field of Database Management Systems (DBMS), and provides an overview of the techniques currently in use amongst the triple store community. The report concludes that for individuals and organisations to be willing to provide large amounts of information as openly-accessible nodes on the Semantic Web, storage and querying of the data must be cheaper and faster than it is currently. Experiences from the DBMS field can be used to maximise triple store performance, and suggestions are provided for lines of investigation in areas of storage, indexing, and query optimisation. Finally, work packages are provided describing expected timetables for further study of these topics

Implications of non-volatile memory as primary storage for database management systems

Traditional Database Management System (DBMS) software relies on hard disks for storing relational data. Hard disks are cheap, persistent, and offer huge storage capacities. However, data retrieval latency for hard disks is extremely high. To hide this latency, DRAM is used as an intermediate storage. DRAM is significantly faster than disk, but deployed in smaller capacities due to cost and power constraints, and without the necessary persistency feature that disks have. Non-Volatile Memory (NVM) is an emerging storage class technology which promises the best of both worlds. It can offer large storage capacities, due to better scaling and cost metrics than DRAM, and is non-volatile (persistent) like hard disks. At the same time, its data retrieval time is much lower than that of hard disks and it is also byte-addressable like DRAM. In this paper, we explore the implications of employing NVM as primary storage for DBMS. In other words, we investigate the modifications necessary to be applied on a traditional relational DBMS to take advantage of NVM features. As a case study, we have modified the storage engine (SE) of PostgreSQL enabling efficient use of NVM hardware. We detail the necessary changes and challenges such modifications entail and evaluate them using a comprehensive emulation platform. Results indicate that our modified SE reduces query execution time by up to 40% and 14.4% when compared to disk and NVM storage, with average reductions of 20.5% and 4.5%, respectively.The research leading to these results has received funding from the European Union’s 7th Framework Programme under grant agreement number 318633, the Ministry of Science and Technology of Spain under contract TIN2015-65316-P, and a HiPEAC collaboration grant awarded to Naveed Ul Mustafa.Peer ReviewedPostprint (author's final draft

SoK: Cryptographically Protected Database Search

Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. Since protected search was introduced in 2000, the area has grown rapidly; systems are offered by academia, start-ups, and established companies. However, there is no best protected search system or set of techniques. Design of such systems is a balancing act between security, functionality, performance, and usability. This challenge is made more difficult by ongoing database specialization, as some users will want the functionality of SQL, NoSQL, or NewSQL databases. This database evolution will continue, and the protected search community should be able to quickly provide functionality consistent with newly invented databases. At the same time, the community must accurately and clearly characterize the tradeoffs between different approaches. To address these challenges, we provide the following contributions: 1) An identification of the important primitive operations across database paradigms. We find there are a small number of base operations that can be used and combined to support a large number of database paradigms. 2) An evaluation of the current state of protected search systems in implementing these base operations. This evaluation describes the main approaches and tradeoffs for each base operation. Furthermore, it puts protected search in the context of unprotected search, identifying key gaps in functionality. 3) An analysis of attacks against protected search for different base queries. 4) A roadmap and tools for transforming a protected search system into a protected database, including an open-source performance evaluation platform and initial user opinions of protected search.Comment: 20 pages, to appear to IEEE Security and Privac

Challenging Ubiquitous Inverted Files

Stand-alone ranking systems based on highly optimized inverted file structures are generally considered ‘the’ solution for building search engines. Observing various developments in software and hardware, we argue however that IR research faces a complex engineering problem in the quest for more flexible yet efficient retrieval systems. We propose to base the development of retrieval systems on ‘the database approach’: mapping high-level declarative specifications of the retrieval process into efficient query plans. We present the Mirror DBMS as a prototype implementation of a retrieval system based on this approach