scispace - formally typeset
Search or ask a question
Author

Mitch Cherniack

Other affiliations: Brown University
Bio: Mitch Cherniack is an academic researcher from Brandeis University. The author has contributed to research in topics: Query optimization & Tuple. The author has an hindex of 26, co-authored 41 publications receiving 9603 citations. Previous affiliations of Mitch Cherniack include Brown University.

Papers
More filters
Proceedings Article
01 Jan 2005
TL;DR: This paper outlines the basic design and functionality of Borealis, and presents a highly flexible and scalable QoS-based optimization model that operates across server and sensor networks and a new fault-tolerance model with flexible consistency-availability trade-offs.
Abstract: Borealis is a second-generation distributed stream processing engine that is being developed at Brandeis University, Brown University, and MIT. Borealis inherits core stream processing functionality from Aurora [14] and distribution functionality from Medusa [51]. Borealis modifies and extends both systems in non-trivial and critical ways to provide advanced capabilities that are commonly required by newly-emerging stream processing applications. In this paper, we outline the basic design and functionality of Borealis. Through sample real-world applications, we motivate the need for dynamically revising query results and modifying query specifications. We then describe how Borealis addresses these challenges through an innovative set of features, including revision records, time travel, and control lines. Finally, we present a highly flexible and scalable QoS-based optimization model that operates across server and sensor networks and a new fault-tolerance model with flexible consistency-availability trade-offs.

1,533 citations

Journal ArticleDOI
01 Aug 2003
TL;DR: The basic processing model and architecture of Aurora, a new system to manage data streams for monitoring applications, are described and a stream-oriented set of operators are described.
Abstract: .This paper describes the basic processing model and architecture of Aurora, a new system to manage data streams for monitoring applications. Monitoring applications differ substantially from conventional business data processing. The fact that a software system must process and react to continual inputs from many sources (e.g., sensors) rather than from human operators requires one to rethink the fundamental architecture of a DBMS for this application area. In this paper, we present Aurora, a new DBMS currently under construction at Brandeis University, Brown University, and M.I.T. We first provide an overview of the basic Aurora model and architecture and then describe in detail a stream-oriented set of operators.

1,518 citations

Book ChapterDOI
01 Dec 2018
TL;DR: Preliminary performance data on a subset of TPC-H is presented and it is shown that the system the team is building, C-Store, is substantially faster than popular commercial products.
Abstract: This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. Among the many differences in its design are: storage of data by column rather than by row, careful coding and packing of objects into storage including main memory during query processing, storing an overlapping collection of column-oriented projections, rather than the current fare of tables and indexes, a non-traditional implementation of transactions which includes high availability and snapshot isolation for read-only transactions, and the extensive use of bitmap indexes to complement B-tree structures.We present preliminary performance data on a subset of TPC-H and show that the system we are building, C-Store, is substantially faster than popular commercial products. Hence, the architecture looks very encouraging.

1,063 citations

Proceedings Article
30 Aug 2005
TL;DR: C-Store as mentioned in this paper is a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimised, and it uses bitmap indexes to complement B-tree structures.
Abstract: This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. Among the many differences in its design are: storage of data by column rather than by row, careful coding and packing of objects into storage including main memory during query processing, storing an overlapping collection of column-oriented projections, rather than the current fare of tables and indexes, a non-traditional implementation of transactions which includes high availability and snapshot isolation for read-only transactions, and the extensive use of bitmap indexes to complement B-tree structures.We present preliminary performance data on a subset of TPC-H and show that the system we are building, C-Store, is substantially faster than popular commercial products. Hence, the architecture looks very encouraging.

970 citations

Book ChapterDOI
20 Aug 2002
TL;DR: This paper presents Aurora, a new DBMS that is currently under construction at Brandeis University, Brown University, and M.I.T. and describes the basic system architecture, a stream-oriented set of operators, optimization tactics, and support for real-time operation.
Abstract: This paper introduces monitoring applications, which we will show differ substantially from conventional business data processing. The fact that a software system must process and react to continual inputs from many sources (e.g., sensors) rather than from human operators requires one to rethink the fundamental architecture of a DBMS for this application area. In this paper, we present Aurora, a new DBMS that is currently under construction at Brandeis University, Brown University, and M.I.T. We describe the basic system architecture, a stream-oriented set of operators, optimization tactics, and support for real-time operation.

963 citations


Cited by
More filters
Proceedings Article
01 Jan 2006
TL;DR: Bigtable as mentioned in this paper is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers, including web indexing, Google Earth and Google Finance.
Abstract: Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this article, we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable.

4,843 citations

Proceedings ArticleDOI
Brian F. Cooper1, Adam Silberstein1, Erwin Tam1, Raghu Ramakrishnan1, Russell Sears1 
10 Jun 2010
TL;DR: This work presents the "Yahoo! Cloud Serving Benchmark" (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of cloud data serving systems, and defines a core set of benchmarks and reports results for four widely used systems.
Abstract: While the use of MapReduce systems (such as Hadoop) for large scale data analysis has been widely recognized and studied, we have recently seen an explosion in the number of systems developed for cloud data serving. These newer systems address "cloud OLTP" applications, though they typically do not support ACID transactions. Examples of systems proposed for cloud serving use include BigTable, PNUTS, Cassandra, HBase, Azure, CouchDB, SimpleDB, Voldemort, and many others. Further, they are being applied to a diverse range of applications that differ considerably from traditional (e.g., TPC-C like) serving workloads. The number of emerging cloud serving systems and the wide range of proposed applications, coupled with a lack of apples-to-apples performance comparisons, makes it difficult to understand the tradeoffs between systems and the workloads for which they are suited. We present the "Yahoo! Cloud Serving Benchmark" (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of cloud data serving systems. We define a core set of benchmarks and report results for four widely used systems: Cassandra, HBase, Yahoo!'s PNUTS, and a simple sharded MySQL implementation. We also hope to foster the development of additional cloud benchmark suites that represent other classes of applications by making our benchmark tool available via open source. In this regard, a key feature of the YCSB framework/tool is that it is extensible--it supports easy definition of new workloads, in addition to making it easy to benchmark new systems.

3,276 citations

Journal ArticleDOI
TL;DR: The simple data model provided by Bigtable is described, which gives clients dynamic control over data layout and format, and the design and implementation of Bigtable are described.
Abstract: Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this article, we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable.

3,259 citations

01 Jan 2006
TL;DR: There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99].
Abstract: The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.

2,591 citations

Journal ArticleDOI
01 Mar 2005
TL;DR: This work evaluates issues in the context of TinyDB, a distributed query processor for smart sensor devices, and shows how acquisitional techniques can provide significant reductions in power consumption on the authors' sensor devices.
Abstract: We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices.

2,065 citations