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From Data Mining to Knowledge Discovery in Databases.

by Usama Fayyad , Gregory Piatetsky-Shapiro , Padhraic Smyth - AI Magazine, , 1996
"... ■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in database ..."
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predictive model for estimating the value of future cases). At the core of the process is the application of specific data-mining methods for pattern discovery and extraction. 1 This article begins by discussing the historical context of KDD and data mining and their intersection with other related fields. A

Process-Aware Information Systems: Lessons to be Learned from Process Mining

by W. M. P. van der Aalst
"... A Process-Aware Information System (PAIS) is a software system that manages and executes operational processes involving people, applications, and/or information sources on the basis of process models. Example PAISs are workflow management systems, case-handling systems, enterprise information syste ..."
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-trivial and useful information from event logs. One aspect of process mining is control-flow discovery, i.e., automatically constructing a process model (e.g., a Petri net) describing the causal dependencies between activities. The insights provided by process mining are very valuable for the development of the next

Process Mining by using Event Logs

by Swapnali B. Sonawane, Ravi P. Patki
"... Process mining techniques have usual notable attention within the literature for their ability to help within the redesign of complex processes by mechanically discovering models that specify the events registered in some log traces provided as input. Process mining refers to the extraction of proce ..."
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of process models from event logs. Now real-life processes tend to be less structured and a lot of flexible. Traditional process mining algorithms have issues dealing with such unstructured processes and generate “spaghetti-like " process models that are exhausting to understand. An approach to beat

Behavioral theories and the neurophysiology of reward,

by Wolfram Schultz - Annu. Rev. Psychol. , 2006
"... ■ Abstract The functions of rewards are based primarily on their effects on behavior and are less directly governed by the physics and chemistry of input events as in sensory systems. Therefore, the investigation of neural mechanisms underlying reward functions requires behavioral theories that can ..."
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is variance, and the probability distribution pi is Gaussian. Thus, EU is expressed as f(EV, variance). This procedure uses variance as a measure of uncertainty. Another measure of uncertainty is the entropy of information theory, which might be appropriate to use when dealing with information processing

Efficient Semantic Event Processing: Lessons Learned in User Interface Integration

by Heiko Paulheim - In L. Aroyo et al. (Eds.), The Semantic Web: Research and Applications, 6th European Semantic Web Conference, ESWC 2010 , 2010
"... Abstract. Most approaches to application integration require an unambiguous exchange of events. Ontologies can be used to annotate the events exchanged and thus ensure a common understanding of those events. The domain knowledge formalized in ontologies can also be employed to facilitate more intell ..."
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Abstract. Most approaches to application integration require an unambiguous exchange of events. Ontologies can be used to annotate the events exchanged and thus ensure a common understanding of those events. The domain knowledge formalized in ontologies can also be employed to facilitate more

Processing flows of information: from data stream to complex event processing

by Gianpaolo Cugola, Alessandro Margara - ACM COMPUTING SURVEYS , 2011
"... A large number of distributed applications requires continuous and timely processing of information as it flows from the periphery to the center of the system. Examples include intrusion detection systems which analyze network traffic in real-time to identify possible attacks; environmental monitori ..."
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A large number of distributed applications requires continuous and timely processing of information as it flows from the periphery to the center of the system. Examples include intrusion detection systems which analyze network traffic in real-time to identify possible attacks; environmental

Time prediction based on process mining

by M. H. Schonenberg, M. Song - Information Systems
"... Abstract. Process mining allows for the automated discovery of pro-cess models from event logs. These models provide insights and enable various types of model-based analysis. This paper demonstrates that the discovered process models can be extended with information to pre-dict the completion time ..."
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configurable approach to construct a process model, augment this model with time information learned from earlier instances, and use this to predict e.g. the comple-tion time. To provide meaningful time predictions we use a configurable set of abstractions that allow for a good balance between “overfitting

SMOTEBoost: improving prediction of the minority class in boosting

by Nitesh V. Chawla, Ar Lazarevic, Lawrence O. Hall, Kevin W. Bowyer - In Proceedings of the Principles of Knowledge Discovery in Databases, PKDD-2003 , 2003
"... Abstract. Many real world data mining applications involve learning from imbalanced data sets, where the particular events of interest may be very few when compared to the other classes. Learning from data sets that contain rare events usually produces biased classifiers that have a higher predictiv ..."
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Abstract. Many real world data mining applications involve learning from imbalanced data sets, where the particular events of interest may be very few when compared to the other classes. Learning from data sets that contain rare events usually produces biased classifiers that have a higher

Distributed

by Waqaas Munawar, Olaf L, Muhammad Hamad Alizai, Klaus Wehrle, Rwth Aachen
"... Abstract—Wireless Sensor Networks (WSNs) are deployed for long periods of time, during which a need often arises to dynamically reprogram or retask them. An array of solutions has been proposed to this effect, ranging from full image replacement to virtual machines. However, the capabilities of Tiny ..."
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in the existing user interfaces, remaining transparent to the user. The evaluation shows that our approach imposes almost no performance overhead for loaded application while keeping a smaller memory footprint than other comparable solutions. I.

Distributed Data Mining for Learning Predictive Knowledge

by Marlon Núñez
"... We propose a distributed data mining method that learns to predict future events. The learnt predictive knowledge is generalized from the experiences of multiple situations in parallel and used as a single knowledge source for all situations. A distributed architecture is the most suitable environme ..."
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We propose a distributed data mining method that learns to predict future events. The learnt predictive knowledge is generalized from the experiences of multiple situations in parallel and used as a single knowledge source for all situations. A distributed architecture is the most suitable
Results 1 - 10 of 556