Spatio-Temporal Ensemble Prediction on Mobile Broadband Network Data

Samulevicius, Saulius; Pitarch, Yoann; Pedersen, Torben Bach; Sørensen, Troels B.

doi:10.1109/vtcspring.2013.6692765

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Competitors. CnSC is compared with a previous work, step [13], and the Weka implementation of the Naive Bayes classifier [7]. Evaluated Parameters.…”

Section: Protocolmentioning

confidence: 99%

Using Closed n-set Patterns for Spatio-Temporal Classification

Samulevicius

Pitarch

Pedersen

2014

Data Warehousing and Knowledge Discovery

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 15178The contribution was presented at DaWaK 2014:http://www.dexa.org/dawak2014 Abstract. Today, huge volumes of sensor data are collected from many different sources. One of the most crucial data mining tasks considering this data is the ability to predict and classify data to anticipate trends or failures and take adequate steps. While the initial data might be of limited interest itself, the use of additional information, e.g., latent attributes, spatio-temporal details, etc., can add significant values and interestingness. In this paper we present a classification approach, called Closed n-set Spatio-Temporal Classification (CnSC), which is based on the use of latent attributes, pattern mining, and classification model construction. As the amount of generated patterns is huge, we employ a scalable NoSQL-based graph database for efficient storage and retrieval. By considering hierarchies in the latent attributes, we define pattern and context similarity scores. The classification model for a specific context is constructed by aggregating the most similar patterns. Presented approach CnSC is evaluated with a real dataset and shows competitive results compared with other prediction strategies.

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“…Competitors. CnSC is compared with a previous work, step [13], and the Weka implementation of the Naive Bayes classifier [7]. Evaluated Parameters.…”

Section: Protocolmentioning

confidence: 99%

Using Closed n-set Patterns for Spatio-Temporal Classification

Samulevicius

Pitarch

Pedersen

2014

Data Warehousing and Knowledge Discovery

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Lattice-based Spatio-temporal Ensemble Prediction

Samulevicius

Pitarch

Pedersen

2014

Procedia Computer Science

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With the rapidly increasing deployment of sensor networks, large amounts of time series data are generated. One of the main challenges when dealing with such data is performing accurate predictions in order to address a broad class of application problems, ranging from mobile broadband network (MBN) optimization to preventive maintenance. To this end, time series prediction has been widely addressed by the statistics community. Nevertheless, such approaches fail in performing well when the data are more context-dependent than history-dependent. In this paper, we investigate how latent attributes can be built upon the time series in order to define a spatio-temporal context for predictions. Moreover, such attributes are often hierarchical, leading to multiple potential contexts at different levels of granularity for performing a given prediction. In support of this scenario, we propose the Lattice-Based Spatio-Temporal Ensemble Prediction (LBSTEP) approach, which allows modeling the problem as a multidimensional spatio-temporal prediction. Given an ensemble prediction model, we propose a solution for determining the most appropriate spatio-temporal context that maximizes the global prediction metrics of a set of the time series. LBSTEP is evaluated with a real-world MBN dataset, which exemplifies the intended general application domain of time series data with a strong spatio-temporal component. The experimental results shows that the proposed contextual and multi-granular view of the prediction problem is effective, in terms of both several optimization metrics and the model calculation.

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