The Experts below are selected from a list of 105 Experts worldwide ranked by ideXlab platform

Hasso Plattner - One of the best experts on this subject based on the ideXlab platform.

  • efficient transaction processing for hyrise in Mixed Workload environments
    Very Large Data Bases, 2015
    Co-Authors: David Schwalb, Martin Grund, Martin Faust, Johannes Wust, Hasso Plattner
    Abstract:

    Hyrise is an in-memory storage engine designed for Mixed enterprise Workloads that originally started as a research prototype for hybrid table layouts and basic transaction processing capabilities. This paper presents our incremental improvements and learnings to better support transactional consistency in Mixed Workloads.

  • split dictionaries for in memory column stores in Mixed Workload environments
    Australasian Database Conference, 2014
    Co-Authors: David Schwalb, Martin Faust, Johannes Wust, Markus Dreseler, Hasso Plattner
    Abstract:

    Columnar in-memory databases use dictionary encoding as a compression technique, replacing long and frequently occurring values with short integers. Sorted dictionaries allow for more efficient query processing as comparisons can be performed directly on the compressed data whereas unsorted dictionaries are faster when inserting new values.

  • leveraging enterprise application characteristics to optimize incremental aggregate maintenance in a columnar in memory database
    Database Systems for Advanced Applications, 2014
    Co-Authors: Stephan Muller, Paul Moller, Hasso Plattner
    Abstract:

    An analysis of database Workloads generated by enterprise applications revealed a Mixed Workload of short-running transactional and long-running analytical queries. With the latter type of queries containing many aggregate operations, we implemented an efficient aggregate caching mechanism. But the incremental materialized view maintenance is very costly for aggregate queries joining multiple tables. To overcome this problem, we analyzed the characteristics of enterprise applications with respect to the creation of business objects and their persistence in the database layer. We evaluated how the detected patterns can be leveraged to reduce the join operations between the main and delta partitions of the involved tables in a columnar in-memory database. The resulting performance improvements are significant and close to using the caching mechanism with a denormalized schema.

  • aggregation strategies for columnar in memory databases in a Mixed Workload
    Conference on Information and Knowledge Management, 2011
    Co-Authors: Stephan Muller, Hasso Plattner
    Abstract:

    The recent trend towards analytics on operational data has led to an approach of reunifying online transactional processing and online analytical processing in one single database. The advent of columnar in-memory databases makes this viable and feasible as expensive join and aggregation operations can be performed with superior performance compared to traditional row-oriented databases. This has led to the radical proposal of abandoning materialized aggregate tables and calculate all aggregations on the fly.This PhD research project investigates factors that have an influence on the aggregation performance in columnar in-memory databases. Based on the identified factors, we aim to evaluate different cost model approaches, that are subject to validation with real-life data of large industry customers and their Mixed Workloads. The goal of this project is the design and implementation of an aggregation engine that decides, based on the data and application characteristics, the historic and current Workload and other cost-relevant factors, whether it is beneficial with regards to query performance, but also considering aggregation view maintenance costs, to materialize an aggregate or not.

  • normalization in a Mixed oltp and olap Workload scenario
    TPC Technology Conference, 2011
    Co-Authors: Anja Bog, Alexander Zeier, Kai Sachs, Hasso Plattner
    Abstract:

    The historically introduced separation of online analytical processing (OLAP) from online transaction processing (OLTP) is in question considering the current developments of databases. Column-oriented databases mainly used in the OLAP environment so far, with the addition of in-memory data storage are adapted to accommodate OLTP as well, thus paving the way for Mixed OLTP and OLAP processing. To assess Mixed Workload systems benchmarking has to evolve along with the database technology. Especially in Mixed Workload scenarios the question arises of how to layout the database. In this paper, we present a case study on the impact of database design focusing on normalization with respect to various Workload mixes and database implementations. We use a novel benchmark methodology that provides Mixed OLTP and OLAP Workloads based on a real scenario.

Umesh Dayal - One of the best experts on this subject based on the ideXlab platform.

  • rfeed a Mixed Workload scheduler for enterprise data warehouses
    International Conference on Data Engineering, 2009
    Co-Authors: Abhay Mehta, Chetan Gupta, Song Wang, Umesh Dayal
    Abstract:

    A typical online Business Intelligence (BI) Workload consists of a combination of short, less intensive queries, along with long, resource intensive queries. As such, the longest queries in a typical BI Workload may take several orders of magnitude more time to execute, compared with the shortest queries in the Workload. This makes it challenging to design a good Mixed Workload Scheduler (MWS). In this paper we first define the design criteria that make a 'good' MWS. We then use these criteria to design rFEED, a MWS that is fair, effective, efficient, and differentiated. We simulate real Workloads and compare our rFEED MWS with models of the current best of breed commercial systems. We show that the rFEED MWS works extremely well.

  • fair effective efficient and differentiated scheduling in an enterprise data warehouse
    Extending Database Technology, 2009
    Co-Authors: Chetan Gupta, Abhay Mehta, Song Wang, Umesh Dayal
    Abstract:

    A typical online Business Intelligence (BI) Workload consists of a combination of short, less intensive queries, along with long, resource intensive queries. As such, the longest queries in a typical BI Workload may take several orders of magnitude more time to execute, compared with the shortest queries in the Workload. This makes it challenging to design a good Mixed Workload Scheduler (MWS). In this paper we first define the design criteria that make a 'good' MWS. We then use these criteria to design rFEED, a MWS that is fair, effective, efficient, and differentiated. We simulate real Workloads and compare our rFEED MWS with models of the current best of breed commercial systems. We show that the rFEED MWS works extremely well.

Abhay Mehta - One of the best experts on this subject based on the ideXlab platform.

  • rfeed a Mixed Workload scheduler for enterprise data warehouses
    International Conference on Data Engineering, 2009
    Co-Authors: Abhay Mehta, Chetan Gupta, Song Wang, Umesh Dayal
    Abstract:

    A typical online Business Intelligence (BI) Workload consists of a combination of short, less intensive queries, along with long, resource intensive queries. As such, the longest queries in a typical BI Workload may take several orders of magnitude more time to execute, compared with the shortest queries in the Workload. This makes it challenging to design a good Mixed Workload Scheduler (MWS). In this paper we first define the design criteria that make a 'good' MWS. We then use these criteria to design rFEED, a MWS that is fair, effective, efficient, and differentiated. We simulate real Workloads and compare our rFEED MWS with models of the current best of breed commercial systems. We show that the rFEED MWS works extremely well.

  • fair effective efficient and differentiated scheduling in an enterprise data warehouse
    Extending Database Technology, 2009
    Co-Authors: Chetan Gupta, Abhay Mehta, Song Wang, Umesh Dayal
    Abstract:

    A typical online Business Intelligence (BI) Workload consists of a combination of short, less intensive queries, along with long, resource intensive queries. As such, the longest queries in a typical BI Workload may take several orders of magnitude more time to execute, compared with the shortest queries in the Workload. This makes it challenging to design a good Mixed Workload Scheduler (MWS). In this paper we first define the design criteria that make a 'good' MWS. We then use these criteria to design rFEED, a MWS that is fair, effective, efficient, and differentiated. We simulate real Workloads and compare our rFEED MWS with models of the current best of breed commercial systems. We show that the rFEED MWS works extremely well.

Chetan Gupta - One of the best experts on this subject based on the ideXlab platform.

  • rfeed a Mixed Workload scheduler for enterprise data warehouses
    International Conference on Data Engineering, 2009
    Co-Authors: Abhay Mehta, Chetan Gupta, Song Wang, Umesh Dayal
    Abstract:

    A typical online Business Intelligence (BI) Workload consists of a combination of short, less intensive queries, along with long, resource intensive queries. As such, the longest queries in a typical BI Workload may take several orders of magnitude more time to execute, compared with the shortest queries in the Workload. This makes it challenging to design a good Mixed Workload Scheduler (MWS). In this paper we first define the design criteria that make a 'good' MWS. We then use these criteria to design rFEED, a MWS that is fair, effective, efficient, and differentiated. We simulate real Workloads and compare our rFEED MWS with models of the current best of breed commercial systems. We show that the rFEED MWS works extremely well.

  • fair effective efficient and differentiated scheduling in an enterprise data warehouse
    Extending Database Technology, 2009
    Co-Authors: Chetan Gupta, Abhay Mehta, Song Wang, Umesh Dayal
    Abstract:

    A typical online Business Intelligence (BI) Workload consists of a combination of short, less intensive queries, along with long, resource intensive queries. As such, the longest queries in a typical BI Workload may take several orders of magnitude more time to execute, compared with the shortest queries in the Workload. This makes it challenging to design a good Mixed Workload Scheduler (MWS). In this paper we first define the design criteria that make a 'good' MWS. We then use these criteria to design rFEED, a MWS that is fair, effective, efficient, and differentiated. We simulate real Workloads and compare our rFEED MWS with models of the current best of breed commercial systems. We show that the rFEED MWS works extremely well.

Song Wang - One of the best experts on this subject based on the ideXlab platform.

  • rfeed a Mixed Workload scheduler for enterprise data warehouses
    International Conference on Data Engineering, 2009
    Co-Authors: Abhay Mehta, Chetan Gupta, Song Wang, Umesh Dayal
    Abstract:

    A typical online Business Intelligence (BI) Workload consists of a combination of short, less intensive queries, along with long, resource intensive queries. As such, the longest queries in a typical BI Workload may take several orders of magnitude more time to execute, compared with the shortest queries in the Workload. This makes it challenging to design a good Mixed Workload Scheduler (MWS). In this paper we first define the design criteria that make a 'good' MWS. We then use these criteria to design rFEED, a MWS that is fair, effective, efficient, and differentiated. We simulate real Workloads and compare our rFEED MWS with models of the current best of breed commercial systems. We show that the rFEED MWS works extremely well.

  • fair effective efficient and differentiated scheduling in an enterprise data warehouse
    Extending Database Technology, 2009
    Co-Authors: Chetan Gupta, Abhay Mehta, Song Wang, Umesh Dayal
    Abstract:

    A typical online Business Intelligence (BI) Workload consists of a combination of short, less intensive queries, along with long, resource intensive queries. As such, the longest queries in a typical BI Workload may take several orders of magnitude more time to execute, compared with the shortest queries in the Workload. This makes it challenging to design a good Mixed Workload Scheduler (MWS). In this paper we first define the design criteria that make a 'good' MWS. We then use these criteria to design rFEED, a MWS that is fair, effective, efficient, and differentiated. We simulate real Workloads and compare our rFEED MWS with models of the current best of breed commercial systems. We show that the rFEED MWS works extremely well.