64,482 research outputs found
Native Directly Follows Operator
Typical legacy information systems store data in relational databases.
Process mining is a research discipline that analyzes this data to obtain
insights into processes. Many different process mining techniques can be
applied to data. In current techniques, an XES event log serves as a basis for
analysis. However, because of the static characteristic of an XES event log, we
need to create one XES file for each process mining question, which leads to
overhead and inflexibility. As an alternative, people attempt to perform
process mining directly on the data source using so-called intermediate
structures. In previous work, we investigated methods to build intermediate
structures on source data by executing a basic SQL query on the database.
However, the nested form in the SQL query can cause performance issues on the
database side. Therefore, in this paper, we propose a native SQL operator for
direct process discovery on relational databases. We define a native operator
for the simplest form of the intermediate structure, called the "directly
follows relation". This approach has been evaluated with big event data and the
experimental results show that it performs faster than the state-of-the-art of
database approaches.Comment: 12 page
Relational Algebra for In-Database Process Mining
The execution logs that are used for process mining in practice are often
obtained by querying an operational database and storing the result in a flat
file. Consequently, the data processing power of the database system cannot be
used anymore for this information, leading to constrained flexibility in the
definition of mining patterns and limited execution performance in mining large
logs. Enabling process mining directly on a database - instead of via
intermediate storage in a flat file - therefore provides additional flexibility
and efficiency. To help facilitate this ideal of in-database process mining,
this paper formally defines a database operator that extracts the 'directly
follows' relation from an operational database. This operator can both be used
to do in-database process mining and to flexibly evaluate process mining
related queries, such as: "which employee most frequently changes the 'amount'
attribute of a case from one task to the next". We define the operator using
the well-known relational algebra that forms the formal underpinning of
relational databases. We formally prove equivalence properties of the operator
that are useful for query optimization and present time-complexity properties
of the operator. By doing so this paper formally defines the necessary
relational algebraic elements of a 'directly follows' operator, which are
required for implementation of such an operator in a DBMS
Towards Efficient Path Query on Social Network with Hybrid RDF Management
The scalability and exibility of Resource Description Framework(RDF) model
make it ideally suited for representing online social networks(OSN). One basic
operation in OSN is to find chains of relations,such as k-Hop friends. Property
path query in SPARQL can express this type of operation, but its implementation
suffers from performance problem considering the ever growing data size and
complexity of OSN.In this paper, we present a main memory/disk based hybrid RDF
data management framework for efficient property path query. In this hybrid
framework, we realize an efficient in-memory algebra operator for property path
query using graph traversal, and estimate the cost of this operator to
cooperate with existing cost-based optimization. Experiments on benchmark and
real dataset demonstrated that our approach can achieve a good tradeoff between
data load expense and online query performance
Impliance: A Next Generation Information Management Appliance
ably successful in building a large market and adapting to the changes of the
last three decades, its impact on the broader market of information management
is surprisingly limited. If we were to design an information management system
from scratch, based upon today's requirements and hardware capabilities, would
it look anything like today's database systems?" In this paper, we introduce
Impliance, a next-generation information management system consisting of
hardware and software components integrated to form an easy-to-administer
appliance that can store, retrieve, and analyze all types of structured,
semi-structured, and unstructured information. We first summarize the trends
that will shape information management for the foreseeable future. Those trends
imply three major requirements for Impliance: (1) to be able to store, manage,
and uniformly query all data, not just structured records; (2) to be able to
scale out as the volume of this data grows; and (3) to be simple and robust in
operation. We then describe four key ideas that are uniquely combined in
Impliance to address these requirements, namely the ideas of: (a) integrating
software and off-the-shelf hardware into a generic information appliance; (b)
automatically discovering, organizing, and managing all data - unstructured as
well as structured - in a uniform way; (c) achieving scale-out by exploiting
simple, massive parallel processing, and (d) virtualizing compute and storage
resources to unify, simplify, and streamline the management of Impliance.
Impliance is an ambitious, long-term effort to define simpler, more robust, and
more scalable information systems for tomorrow's enterprises.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
A layered framework for pattern-based ontology evolution
The challenge of ontology-driven modelling of information
components is well known in both academia and industry. In this paper, we present a novel approach to deal with customisation and abstraction of ontology-based model evolution. As a result of an empirical study, we identify a layered change operator framework based on the granularity,
domain-speciļ¬city and abstraction of changes. The implementation of the operator framework is supported through layered change logs. Layered change logs capture the objective of ontology changes at a higher level of granularity and support a comprehensive understanding of ontology evolution. The layered change logs are formalised using a graph-based approach. We identify the recurrent ontology change patterns from an ontology change log for their reuse. The identiļ¬ed patterns facilitate optimizing and improving the deļ¬nition of domain-speciļ¬c change patterns
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