VerdictDB: Universalizing Approximate Query Processing

Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of traditional vendors to make radical changes to their legacy codebases, and the preoccupation of newer vendors (e.g., SQL-on-Hadoop products) with implementing standard features. Additionally, the few AQP engines that are available are each tied to a specific platform and require users to completely abandon their existing databases---an unrealistic expectation given the infancy of the AQP technology. Therefore, we argue that a universal solution is needed: a database-agnostic approximation engine that will widen the reach of this emerging technology across various platforms. Our proposal, called VerdictDB, uses a middleware architecture that requires no changes to the backend database, and thus, can work with all off-the-shelf engines. Operating at the driver-level, VerdictDB intercepts analytical queries issued to the database and rewrites them into another query that, if executed by any standard relational engine, will yield sufficient information for computing an approximate answer. VerdictDB uses the returned result set to compute an approximate answer and error estimates, which are then passed on to the user or application. However, lack of access to the query execution layer introduces significant challenges in terms of generality, correctness, and efficiency. This paper shows how VerdictDB overcomes these challenges and delivers up to 171$\times$ speedup (18.45$\times$ on average) for a variety of existing engines, such as Impala, Spark SQL, and Amazon Redshift, while incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache License.Comment: Extended technical report of the paper that appeared in Proceedings of the 2018 International Conference on Management of Data, pp. 1461-1476. ACM, 201

Proximal business intelligence on the semantic web

This is the post-print version of this article. The official version can be accessed from the link below - Copyright @ 2010 Springer.Ubiquitous information systems (UBIS) extend current Information System thinking to explicitly differentiate technology between devices and software components with relation to people and process. Adapting business data and management information to support specific user actions in context is an ongoing topic of research. Approaches typically focus on providing mechanisms to improve specific information access and transcoding but not on how the information can be accessed in a mobile, dynamic and ad-hoc manner. Although web ontology has been used to facilitate the loading of data warehouses, less research has been carried out on ontology based mobile reporting. This paper explores how business data can be modeled and accessed using the web ontology language and then re-used to provide the invisibility of pervasive access; uncovering more effective architectural models for adaptive information system strategies of this type. This exploratory work is guided in part by a vision of business intelligence that is highly distributed, mobile and fluid, adapting to sensory understanding of the underlying environment in which it operates. A proof-of concept mobile and ambient data access architecture is developed in order to further test the viability of such an approach. The paper concludes with an ontology engineering framework for systems of this type – named UBIS-ONTO

A Big Data Analyzer for Large Trace Logs

Current generation of Internet-based services are typically hosted on large data centers that take the form of warehouse-size structures housing tens of thousands of servers. Continued availability of a modern data center is the result of a complex orchestration among many internal and external actors including computing hardware, multiple layers of intricate software, networking and storage devices, electrical power and cooling plants. During the course of their operation, many of these components produce large amounts of data in the form of event and error logs that are essential not only for identifying and resolving problems but also for improving data center efficiency and management. Most of these activities would benefit significantly from data analytics techniques to exploit hidden statistical patterns and correlations that may be present in the data. The sheer volume of data to be analyzed makes uncovering these correlations and patterns a challenging task. This paper presents BiDAl, a prototype Java tool for log-data analysis that incorporates several Big Data technologies in order to simplify the task of extracting information from data traces produced by large clusters and server farms. BiDAl provides the user with several analysis languages (SQL, R and Hadoop MapReduce) and storage backends (HDFS and SQLite) that can be freely mixed and matched so that a custom tool for a specific task can be easily constructed. BiDAl has a modular architecture so that it can be extended with other backends and analysis languages in the future. In this paper we present the design of BiDAl and describe our experience using it to analyze publicly-available traces from Google data clusters, with the goal of building a realistic model of a complex data center.Comment: 26 pages, 10 figure

Duplicate Detection in Probabilistic Data

Collected data often contains uncertainties. Probabilistic databases have been proposed to manage uncertain data. To combine data from multiple autonomous probabilistic databases, an integration of probabilistic data has to be performed. Until now, however, data integration approaches have focused on the integration of certain source data (relational or XML). There is no work on the integration of uncertain (esp. probabilistic) source data so far. In this paper, we present a first step towards a concise consolidation of probabilistic data. We focus on duplicate detection as a representative and essential step in an integration process. We present techniques for identifying multiple probabilistic representations of the same real-world entities. Furthermore, for increasing the efficiency of the duplicate detection process we introduce search space reduction methods adapted to probabilistic data

Computing Possible and Certain Answers over Order-Incomplete Data

This paper studies the complexity of query evaluation for databases whose relations are partially ordered; the problem commonly arises when combining or transforming ordered data from multiple sources. We focus on queries in a useful fragment of SQL, namely positive relational algebra with aggregates, whose bag semantics we extend to the partially ordered setting. Our semantics leads to the study of two main computational problems: the possibility and certainty of query answers. We show that these problems are respectively NP-complete and coNP-complete, but identify tractable cases depending on the query operators or input partial orders. We further introduce a duplicate elimination operator and study its effect on the complexity results.Comment: 55 pages, 56 references. Extended journal version of arXiv:1707.07222. Up to the stylesheet, page/environment numbering, and possible minor publisher-induced changes, this is the exact content of the journal paper that will appear in Theoretical Computer Scienc

Implementation of a data management software system for SSME test history data

The implementation of a software system for managing Space Shuttle Main Engine (SSME) test/flight historical data is presented. The software system uses the database management system RIM7 for primary data storage and routine data management, but includes several FORTRAN programs, described here, which provide customized access to the RIM7 database. The consolidation, modification, and transfer of data from the database THIST, to the RIM7 database THISRM is discussed. The RIM7 utility modules for generating some standard reports from THISRM and performing some routine updating and maintenance are briefly described. The FORTRAN accessing programs described include programs for initial loading of large data sets into the database, capturing data from files for database inclusion, and producing specialized statistical reports which cannot be provided by the RIM7 report generator utility. An expert system tutorial, constructed using the expert system shell product INSIGHT2, is described. Finally, a potential expert system, which would analyze data in the database, is outlined. This system could use INSIGHT2 as well and would take advantage of RIM7's compatibility with the microcomputer database system RBase 5000

CC-interop : COPAC/Clumps Continuing Technical Cooperation. Final Project Report

As far as is known, CC-interop was the first project of its kind anywhere in the world and still is. Its basic aim was to test the feasibility of cross-searching between physical and virtual union catalogues, using COPAC and the three functioning "clumps" or virtual union catalogues (CAIRNS, InforM25, and RIDING), all funded or part-funded by JISC in recent years. The key issues investigated were technical interoperability of catalogues, use of collection level descriptions to search union catalogues dynamically, quality of standards in cataloguing and indexing practices, and usability of union catalogues for real users. The conclusions of the project were expected to, and indeed do, contribute to the development of the JISC Information Environment and to the ongoing debate as to the feasibility and desirability of creating a national UK catalogue. They also inhabit the territory of collection level descriptions (CLDs) and the wider services of JISC's Information Environment Services Registry (IESR). The results of this project will also have applicability for the common information environment, particularly through the landscaping work done via SCONE/CAIRNS. This work is relevant not just to HE and not just to digital materials, but encompasses other sectors and domains and caters for print resources as well. Key findings are thematically grouped as follows: System performance when inter-linking COPAC and the Z39.50 clumps. The various individual Z39.50 configurations permit technical interoperability relatively easily but only limited semantic interoperability is possible. Disparate cataloguing and indexing practices are an impairment to semantic interoperability, not just for catalogues but also for CLDs and descriptions of services (like those constituting JISC's IESR). Creating dynamic landscaping through CLDs: routines can be written to allow collection description databases to be output in formats that other UK users of CLDs, including developers of the JISC information environment. Searching a distributed (virtual) catalogue or clump via Z39.50: use of Z39.50 to Z39.50 middleware permits a distributed catalogue to be searched via Z39.50 from such disparate user services as another virtual union catalogue or clump, a physical union catalogue like COPAC, an individual Z client and other IE services. The breakthrough in this Z39.50 to Z39.50 conundrum came with the discovery that the JISC-funded JAFER software (a result of the 5/99 programme) meets many of the requirements and can be used by the current clumps services. It is technically possible for the user to select all or a sub-set of available end destination Z39.50 servers (we call this "landscaping") within this middleware. Comparing results processing between COPAC and clumps. Most distributed services (clumps) do not bring back complete results sets from associated Z servers (in order to save time for users). COPAC on-the-fly routines could feasibly be applied to the clumps services. An automated search set up to repeat its query of 17 catalogues in a clump (InforM25) hourly over nearly 3 months returned surprisingly good results; for example, over 90% of responses were received in less than one second, and no servers showed slower response times in periods of traditionally heavy OPAC use (mid-morning to early evening). User behaviour when cross-searching catalogues: the importance to users of a number of on-screen features, including the ability to refine a search and clear indication that a search is processing. The importance to users of information about the availability of an item as well as the holdings data. The impact of search tools such as Google and Amazon on user behaviour and the expectations of more information than is normally available from a library catalogue. The distrust of some librarians interviewed of the data sources in virtual union catalogues, thinking that there was not true interoperability