Trio-One: Layering Uncertainty and Lineage on a Conventional DBMS

Trio is a new kind of database system that supports data, uncertainty, and lineage in a fully integrated manner. The first Trio prototype, dubbed Trio-One, is built on top of a conventional DBMS using data and query translation techniques together with a small number of stored procedures. This paper describes Trio-One's translation scheme and system architecture, showing how it efficiently and easily supports the Trio data model and query language

S-Store: Streaming Meets Transaction Processing

Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees

Middleware-based Database Replication: The Gaps between Theory and Practice

The need for high availability and performance in data management systems has been fueling a long running interest in database replication from both academia and industry. However, academic groups often attack replication problems in isolation, overlooking the need for completeness in their solutions, while commercial teams take a holistic approach that often misses opportunities for fundamental innovation. This has created over time a gap between academic research and industrial practice. This paper aims to characterize the gap along three axes: performance, availability, and administration. We build on our own experience developing and deploying replication systems in commercial and academic settings, as well as on a large body of prior related work. We sift through representative examples from the last decade of open-source, academic, and commercial database replication systems and combine this material with case studies from real systems deployed at Fortune 500 customers. We propose two agendas, one for academic research and one for industrial R&D, which we believe can bridge the gap within 5-10 years. This way, we hope to both motivate and help researchers in making the theory and practice of middleware-based database replication more relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, June 200

Database integrated analytics using R : initial experiences with SQL-Server + R

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Most data scientists use nowadays functional or semi-functional languages like SQL, Scala or R to treat data, obtained directly from databases. Such process requires to fetch data, process it, then store again, and such process tends to be done outside the DB, in often complex data-flows. Recently, database service providers have decided to integrate “R-as-a-Service” in their DB solutions. The analytics engine is called directly from the SQL query tree, and results are returned as part of the same query. Here we show a first taste of such technology by testing the portability of our ALOJA-ML analytics framework, coded in R, to Microsoft SQL-Server 2016, one of the SQL+R solutions released recently. In this work we discuss some data-flow schemes for porting a local DB + analytics engine architecture towards Big Data, focusing specially on the new DB Integrated Analytics approach, and commenting the first experiences in usability and performance obtained from such new services and capabilities.Peer ReviewedPostprint (author's final draft

Rephrasing rules for off-the-shelf SQL database servers

We have reported previously (Gashi et al., 2004) results of a study with a sample of bug reports from four off-the-shelf SQL servers. We checked whether these bugs caused failures in more than one server. We found that very few bugs caused failures in two servers and none caused failures in more than two. This would suggest a fault-tolerant server built with diverse off-the-shelf servers would be a prudent choice for improving failure detection. To study other aspects of fault tolerance, namely failure diagnosis and state recovery, we have studied the "data diversity" mechanism and we defined a number of SQL rephrasing rules. These rules transform a client sent statement to an additional logically equivalent statement, leading to more results being returned to an adjudicator. These rules therefore help to increase the probability of a correct response being returned to a client and maintain a correct state in the database

bdbms -- A Database Management System for Biological Data

Biologists are increasingly using databases for storing and managing their data. Biological databases typically consist of a mixture of raw data, metadata, sequences, annotations, and related data obtained from various sources. Current database technology lacks several functionalities that are needed by biological databases. In this paper, we introduce bdbms, an extensible prototype database management system for supporting biological data. bdbms extends the functionalities of current DBMSs to include: (1) Annotation and provenance management including storage, indexing, manipulation, and querying of annotation and provenance as first class objects in bdbms, (2) Local dependency tracking to track the dependencies and derivations among data items, (3) Update authorization to support data curation via content-based authorization, in contrast to identity-based authorization, and (4) New access methods and their supporting operators that support pattern matching on various types of compressed biological data types. This paper presents the design of bdbms along with the techniques proposed to support these functionalities including an extension to SQL. We also outline some open issues in building bdbms.Comment: This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but, you must attribute the work to the author and CIDR 2007. 3rd Biennial Conference on Innovative Data Systems Research (CIDR) January 710, 2007, Asilomar, California, US

Developing efficient web-based GIS applications

There is an increase in the number of web-based GIS applications over the recent years. This paper describes different mapping technologies, database standards, and web application development standards that are relevant to the development of web-based GIS applications. Different mapping technologies for displaying geo-referenced data are available and can be used in different situations. This paper also explains why Oracle is the system of choice for geospatial applications that need to handle large amounts of data. Wireframing and design patterns have been shown to be useful in making GIS web applications efficient, scalable and usable, and should be an important part of every web-based GIS application. A range of different development technologies are available, and their use in different operating environments has been discussed here in some detail