Pocket Data Mining: Big Data on Small Devices (Studies in Big Data)
Frederic Stahl, Joao Bártolo Gomes
Language: English
Pages: 108
ISBN: 3319027107
Format: PDF / Kindle (mobi) / ePub
Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.
network searching for data sources that are relevant for the data mining task, and for mobile devices that fulfill the computational requirements (battery life, memory capacity, processing power, etc.). While the MRD is collecting this information, it will decide on the best combination of 26 3 Pocket Data Mining Framework techniques to perform the data mining task. On its return to the task initiator, the MRD will decide which AMs need to be deployed to remote mobile devices. There might be
discusses collaborative data stream mining in ubiquitous environments and describes Coll-Stream, an ensemble approach that incrementally learns which classifiers from an ensemble are more accurate for certain regions of classification problem the feature space. Coll-Stream is able to adapt to changes in the underlying concept using a sliding window of the classifier estimates for each region. Moreover, we also discussed the possible variations of Coll-Stream. Coll-Stream represents an important
in MOA as base classifiers, for consistency 72 6 Experimental Validation of Context-Aware PDM with experiments reported in Chapter 3 for the base version of PDM. Therefore, for each concept the ensemble receives 2 classifiers. For the real datasets, the ensemble only receives the 3 first of the 4 possible concepts, this asserts how the approach is able to adapt the existing knowledge to a new concept that is not similar to the ones available in the community (note that for each still two
with other related methods in terms of accuracy, noise, partition granularity and concept similarity in relation to the local underlying concept. 80 6 Experimental Validation of Context-Aware PDM The experimental results show that the Coll-Stream approach mostly outperforms the other methods and could be used for situations of collaborative data stream mining as it is able to exploit local knowledge from other concepts that are similar to the new underlying concept. This chapter brings a
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