%Titelseite


% The following data (volume, number and page) are given by the editors prior to publishing your paper
\jidmVolume{2}
\jidmNumber{3}
\jidmYear{11}
\jidmMonth{October}
\setcounter{page}{1}


% Includes headers with simplified name of the authors and paper title
\markboth{P. Reimann, H. Schwarz, and B. Mitschang}
{Design, Implementation, and Evaluation of a Tight Integration of Database and Workflow Engines}
%  -> \markboth{}{}
%         takes 2 arguments
%         ex: \markboth{M. M. Moro}{Any paper title}


% Title of the paper
\title{Design, Implementation, and Evaluation of a Tight Integration of Database and Workflow Engines}


% List of authors
%IF THERE ARE TWO or more institutions, please use:
%\author{Name of Author1\inst{1}, Name of Author2\inst{2}, Name of Author3\inst{2}}
\author{Peter Reimann, Holger Schwarz, and Bernhard Mitschang}


%Affiliation and email
\institute{Institute of Parallel and Distributed Systems, University of Stuttgart, Germany \\ \email{\{Firstname.Lastname\}@ipvs.uni-stuttgart.de}
% IF THERE IS ANOTHER INSTITUTION:
%\and Name_of_the_second_institution \\
%\email{address@whatever.com}
}


% Paper abstract - it should be from 100 to 300 words
\begin{abstract}
Accessing and processing huge amounts of heterogeneous and distributed data are some of the major challenges of data-intensive workflows. Traditionally, the descriptions of such workflows focus on their data flow. Nevertheless, control-flow-oriented workflow languages are increasingly adapted to the needs of data-intensive workflows. This provides a common level of abstraction for both data-intensive workflows and classical orchestration workflows, \eg business workflows, which then enables a comprehensive optimization across all workflows. However, the problem still remains that workflows described in control-flow-oriented languages tend to be less efficient for data-intensive processes compared to specialized data-flow-oriented approaches. In this paper, we propose a new kind of optimization targeted at data-intensive workflows that are described in control-flow-oriented languages. We show how to improve efficiency of such workflows by introducing various techniques that partition the local data processing tasks to be performed during workflow execution in an improved way. These data processing tasks are either assigned to the workflow engine or to the tightly integrated local database engine. We evaluate the effectiveness of these techniques via various test scenarios.
%optimize the local data processing in such workflows and introduce various techniques that partition the data processing tasks to be performed during workflow execution in an optimal way. These data processing tasks are either assigned to the workflow engine or to the tightly integrated local database engine. We evaluate the effectiveness of the optimization techniques by means of various test scenarios.
\end{abstract}


% ACM Computing Classification System categories
\category{D.2.11}{Software Engineering}{Software Architectures}
\category{H.2.8}{Information Systems}{Database Applications}
\category{H.4.1}{Information Systems Applications}{Office Automation}

% Categories and Descriptors are available at the 1998 ACM Computing Classification System
% http://www.acm.org/about/class/1998/
%  -> \category{}{}{}
%         takes 3 arguments for the Computing Reviews Classification Scheme.
%         ex: \category{D.3.3}{Programming Languages}{Language Constructs and Features}
%                   [data types and structures]
%                   the last argument, in square brackets, is optional.

% Paper keywords
\keywords{Data-Intensive Workflow; Improved Local Data Processing; Scientific Workflow; Simulation Workflow}
%  -> \keywords{} (in alphabetical order \keywords{document processing, sequences,
%                      string searching, subsequences, substrings})