79,588 research outputs found
Heuristics Miners for Streaming Event Data
More and more business activities are performed using information systems.
These systems produce such huge amounts of event data that existing systems are
unable to store and process them. Moreover, few processes are in steady-state
and due to changing circumstances processes evolve and systems need to adapt
continuously. Since conventional process discovery algorithms have been defined
for batch processing, it is difficult to apply them in such evolving
environments. Existing algorithms cannot cope with streaming event data and
tend to generate unreliable and obsolete results.
In this paper, we discuss the peculiarities of dealing with streaming event
data in the context of process mining. Subsequently, we present a general
framework for defining process mining algorithms in settings where it is
impossible to store all events over an extended period or where processes
evolve while being analyzed. We show how the Heuristics Miner, one of the most
effective process discovery algorithms for practical applications, can be
modified using this framework. Different stream-aware versions of the
Heuristics Miner are defined and implemented in ProM. Moreover, experimental
results on artificial and real logs are reported
Sectoral Trends and Cycles in Germany
We examine the comovements between the output indexes of three German sectors (manufacturing, mining, and agriculture) and the three corresponding sectoral stock market indexes. It is found that data with and without seasonal adjustment give mixed results on the long-run interaction between the sectoral indexes. Compared with data that are non-seasonally adjusted, the adjusted data offer a weaker evidence on the cointegration relationship between a) the sectoral output indexes, b) sectoral stock indexes, and c) individual pairs of real and financial indexes. On short-run comovement, seasonally adjusted data offer stronger evidence on the presence of common synchronized and non-synchronized cyclical components.
A sectoral analysis of Barbados’ GDP business cycle
This paper has two main objectives. Firstly, to establish and characterise a reference cycle (based on real output) for Barbados over the quarterly period 1974-2003 using the Bry and Boschan algorithm. Secondly, to link this aggregate output cycle to the cycles of the individual sectors that comprises real output. The overriding conclusions are that the cycles of tourism and wholesale and retail closely resembles that of the aggregate business cycle, while the non-sugar agriculture and fishing cycle is acyclical.Barbados; Gross Domestic Product, Business Cycle
Challenges in Complex Systems Science
FuturICT foundations are social science, complex systems science, and ICT.
The main concerns and challenges in the science of complex systems in the
context of FuturICT are laid out in this paper with special emphasis on the
Complex Systems route to Social Sciences. This include complex systems having:
many heterogeneous interacting parts; multiple scales; complicated transition
laws; unexpected or unpredicted emergence; sensitive dependence on initial
conditions; path-dependent dynamics; networked hierarchical connectivities;
interaction of autonomous agents; self-organisation; non-equilibrium dynamics;
combinatorial explosion; adaptivity to changing environments; co-evolving
subsystems; ill-defined boundaries; and multilevel dynamics. In this context,
science is seen as the process of abstracting the dynamics of systems from
data. This presents many challenges including: data gathering by large-scale
experiment, participatory sensing and social computation, managing huge
distributed dynamic and heterogeneous databases; moving from data to dynamical
models, going beyond correlations to cause-effect relationships, understanding
the relationship between simple and comprehensive models with appropriate
choices of variables, ensemble modeling and data assimilation, modeling systems
of systems of systems with many levels between micro and macro; and formulating
new approaches to prediction, forecasting, and risk, especially in systems that
can reflect on and change their behaviour in response to predictions, and
systems whose apparently predictable behaviour is disrupted by apparently
unpredictable rare or extreme events. These challenges are part of the FuturICT
agenda
The WHY in Business Processes: Discovery of Causal Execution Dependencies
A crucial element in predicting the outcomes of process interventions and
making informed decisions about the process is unraveling the genuine
relationships between the execution of process activities. Contemporary process
discovery algorithms exploit time precedence as their main source of model
derivation. Such reliance can sometimes be deceiving from a causal perspective.
This calls for faithful new techniques to discover the true execution
dependencies among the tasks in the process. To this end, our work offers a
systematic approach to the unveiling of the true causal business process by
leveraging an existing causal discovery algorithm over activity timing. In
addition, this work delves into a set of conditions under which process mining
discovery algorithms generate a model that is incongruent with the causal
business process model, and shows how the latter model can be methodologically
employed for a sound analysis of the process. Our methodology searches for such
discrepancies between the two models in the context of three causal patterns,
and derives a new view in which these inconsistencies are annotated over the
mined process model. We demonstrate our methodology employing two open process
mining algorithms, the IBM Process Mining tool, and the LiNGAM causal discovery
technique. We apply it on a synthesized dataset and on two open benchmark data
sets.Comment: 20 pages, 19 figure
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Mining learning preferences in web-based instruction: Holists vs. Serialists
Web-based instruction programs are used by learners with diverse knowledge, skills and needs. These differences determine their preferences for the design of Web-based instruction programs and ultimately influence learners' success in using them. Cognitive style has been found to significantly affect learners' preferences of web-based instruction programs. However, the majority of previous studies focus on Field Dependence/Independence. Pask's Holist/Serialist dimension has conceptual links with Field Dependence/Independence but it is left mostly unstudied. Therefore, this study focuses on identifying how this dimension of cognitive style affects learner preferences of Web-based instruction programs. A data mining approach is used to illustrate the difference in preferences between Holists and Serialists. The findings show that there are clear differences in regard to content presentation and navigation support. A set of design features were then produced to help designers incorporate cognitive styles into the development of Web-based instruction programs to ensure that they can accommodate learners' different preferences.This work is partially funded by National Science Council, Taiwan, ROC (NSC 98-2511-S-008-012- MY3; NSC 99-
2511-S-008 -003 -MY2; NSC 99-2631-S-008-001)
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