View source for DMIP 2017

{{Event
|Acronym=DMIP 2017
|Title=Workshop on Data Mining in Practice: Automation and Cost
|Type=Workshop
|Field=Data mining
|Start date=2017/11/18
|End date=2017/11/18
|Submission deadline=2017/09/07
|Homepage=https://sites.google.com/site/icdm17dmip/home
|City=New Orleans
|State=Louisiana
|Country=USA
}}
Workshop is part of [[ICDM 2017]]


==Workshop Description==
All fields where data is collected have seen an increase in the amount of data
being analyzed, and this has led to an increase in work related to data mining
and machine learning. These methods are moving out of academic and high tech
fields and into new and everyday applications. This half day workshop held in
conjunction with the 2017 IEEE International Conference on Data Mining (ICDM)
will focus on two primary issues when applying data mining in practice: how to
incorporate the cost of data into the problem and how to automate aspects of the
data mining process.

==Cost==
From a data perspective, cost can be divided into three primary areas: 1)
collection cost – costs associated with acquiring each data stream, 2) labeling
costs – costs associated with assigning classes, acquiring response variable
values in supervised learning, or acquiring the values of missing explanatory
data, and 3) processing costs – costs associated with model training,
prediction, and storage. In the data mining literature, algorithms and models
are usually optimized with respect to predictive accuracy and little is
published on incorporating costs into the data mining process. However, in
almost all real-world data mining applications, costs are present and should be
considered. In order to data mining applications to be successful, they must
meet the system requirements and objectives while not exceeding the budget.

==Automation==
There have been many debates in recent years about the need and the ability to
automate data mining and machine learning tasks. A recent blog post titled “Data
Scientists Need More Automation” discusses the repeated efforts required to
configure and run services or scripts on a network of machines. Other
discussions ask, “Can We Automate Data Mining?,” arguing that many tasks
performed by data scientists require manual intervention and thus cannot be
automated; in other words, expertise is needed for each individual case,
requiring clear understanding of the business and the data. The development of
tools to automate data mining efforts fosters the transformation of theory to
application and also promotes the development of standards and the adoption of
these standards. Automated standards enable researchers and practitioners to
better communicate, sharing successes and challenges in a more consistent common
language. In an age of software as a service and ever-increasing scalability
requirements, standards are necessary. Consistent adoption, application, and
communication in turn promote research and refinement of the automated
strategies and growth of the community. The challenges that must be discussed
relate to the boundaries of automated tasks and individual attention needed for
each unique business and data scenario.


==Topics include (but are not limited to):==

Automation
* Automated methods in machine learning, data mining, predictive analytics, and deep learning
* Automated methods in knowledge discovery in databases
* Automation theory, automation and optimization
* Hyperparameter autotuning
* Automated pipelines and process-flows in production systems
* Automated approaches to model monitoring and updating
* Automated methods for streaming data
* Internet of Things (IoT) and automation
* Automated data preparation, automated variable and model selection
* Automation in big data, automation in massive modeling
* 
Cost
* Active learning and cost
* Missing data algorithms
* Feature selection and cost
* Efficient feature engineering
* Algorithm processing time analysis
* Model training algorithms that incorporate costs
* Data mining on a budget applications in real-world systems and environments
* Theory of data mining on a budget
* Metrics that combine accuracy and cost
Acronym	DMIP 2017 +
End date	November 18, 2017 +
Event type	Workshop +
Has coordinates	29° 57' 0", -90° 4' 12"Latitude: 29.949933333333 Longitude: -90.070116666667 +
Has location city	New Orleans +
Has location country	Category:USA +
Has location state	Louisiana +
Homepage	https://sites.google.com/site/icdm17dmip/home +
IsA	Event +
Start date	November 18, 2017 +
Submission deadline	September 7, 2017 +
Title	Workshop on Data Mining in Practice: Automation and Cost +