MOA Massive Online Analysis

Real Time Analytics for Data Streams

Author: admin

New Release of MOA 14.11

We’ve made a new release of MOA 14.11.

The new features of this release are:

  • Lazy kNN methods.
    • Albert Bifet, Bernhard Pfahringer, Jesse Read, Geoff Holmes: Efficient data stream classification via probabilistic adaptive windows. SAC 2013: 801-806
  • SGDMultiClass for multi-class SGD learning.
  • OnlineSmoothBoost
    • Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu:An Online Boosting Algorithm with Theoretical Justifications. ICML 2012
  • ReplacingMissingValuesFilter: a filter to replace missing values by Manuel Martin Salvador.
  • HDDM Concept Drift detector
    • I. Frias-Blanco, J. del Campo-Avila, G. Ramos-Jimenez, R. Morales-Bueno, A. Ortiz-Diaz, and Y. Caballero-Mota, Online and non-parametric drift detection methods based on Hoeffding’s bound, IEEE Transactions on Knowledge and Data Engineering, 2014.
  • SeqDriftChangeDetector Concept Drift detector
    • Pears, R., Sakthithasan, S., & Koh, Y. (2014). Detecting concept change in dynamic data streams. Machine Learning, 97(3), 259-293.
  • Updates:
    • SGD, HoeffdingOptionTree, HAT, FIMTDD, Change Detectors, and DACC

You find the download link for this release on the MOA homepage:

MOA (Massive Online Analysis)

Cheers,

The MOA Team

New release of MOA 13.08

We’ve made a new release of MOA 13.08.

The new features of this release are:

  • new outlier detection tab
    • Dimitrios Georgiadis, Maria Kontaki, Anastasios Gounaris, Apostolos N. Papadopoulos, Kostas Tsichlas, Yannis Manolopoulos: Continuous outlier detection in data streams: an extensible framework and state-of-the-art algorithms. SIGMOD Conference 2013: 1061-1064
  • new regression tab
  • FIMT-DD regression tree
    • Elena Ikonomovska, João Gama, Saso Dzeroski: Learning model trees from evolving data streams. Data Min. Knowl. Discov. 23(1): 128-168 (2011)
  • Adaptive Model Rules for regression
    • Ezilda Almeida, Carlos Abreu Ferreira, João Gama: Adaptive Model Rules from Data Streams. ECML/PKDD (1) 2013: 480-492
  • a recommender system based in BRISMFPredictor
    • Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk: Scalable Collaborative Filtering Approaches for Large Recommender Systems. Journal of Machine Learning Research 10: 623-656 (2009)
  • clustering updates

You find the download link for this release on the MOA homepage:

MOA (Massive Online Analysis)

Cheers,

The MOA Team

Pre-release of MOA 13.08

We are preparing a new release of MOA 13.08.

The new release of MOA will contain the FIMT-DD regression tree, the Adaptive Model Rules, a recommender system based in BRISMFPredictor, and some more features in clustering and a new outlier detection tab.

You will find the source code at the repository:

https://code.google.com/p/moa/

Cheers,

The MOA Team

CFP – KDD BIGMINE Workshop on Big Data Mining

Big Data Mining
2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine-13)

Conference Dates: August 11-14, 2013
Workshop Date: Aug 11, 2013
Chicago, USA

https://www.bigdata-mining.org

Key dates:
Papers due: June 6th 23:59PM
Acceptance notification: June 25, 2013
Workshop Final Paper Due: July 2, 2013

Paper submission and reviewing will be handled electronically. Authors should consult the submission site (https://bigdata-mining.org/submission/) for full details regarding paper preparation and submission guidelines.

Papers submitted to BigMine-13 should be original work and substantively different from papers that have been previously published or are under review in a journal or another conference/workshop.

Following KDD main conference tradition, reviews are not double-blind, and author names and affiliations should be listed.

We invite submission of papers describing innovative research on all aspects of big data mining.

Examples of topic of interest include

  1. Scalable, Distributed and Parallel Algorithms
  2. New Programming Model for Large Data beyond Hadoop/MapReduce, STORM, streaming languages
  3. Mining Algorithms of Data in non-traditional formats (unstructured, semi-structured)
  4. Applications: social media, Internet of Things, Smart Grid, Smart Transportation
    Systems
  5. Streaming Data Processing
  6. Heterogeneous Sources and Format Mining
  7. Systems Issues related to large datasets: clouds, streaming system, architecture, and issues beyond cloud and streams.
  8. Interfaces to database systems and analytics.
  9. Evaluation Technologies
  10. Visualization for Big Data
  11. Applications: Large scale recommendation systems, social media systems, social network systems, scientific data mining, environmental, urban and other large data mining applications.

Papers emphasizing theoretical foundations, algorithms, systems, applications, language issues, data storage and access, architecture are particularly encouraged.

We welcome submissions by authors who are new to the data mining research community.

Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity of writing. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well executed, and repeatable. Authors are strongly encouraged to make data and code publicly available whenever possible.