Quality-optimized predictive analytics

Abstract

On-line statistical and machine learning analytic tasks over largescale contextual data streams coming from e.g., wireless sensor networks, Internet of Things environments, have gained high popularity nowadays due to their significance in knowledge extraction, regression and classification tasks, and, more generally, in making sense from large-scale streaming data. The quality of the received contextual information, however, impacts predictive analytics tasks especially when dealing with uncertain data, outliers data, and data containing missing values. Low quality of received contextual data significantly spoils the progressive inference and on-line statistical reasoning tasks, thus, bias is introduced in the induced knowledge, e.g., classification and decision making. To alleviate such situation, which is not so rare in real time contextual information processing systems, we propose a progressive time-optimized data quality-aware mechanism, which attempts to deliver contextual information of high quality to predictive analytics engines by progressively introducing a certain controlled delay. Such a mechanism progressively delivers high quality data as much as possible, thus eliminating possible biases in knowledge extraction and predictive analysis tasks. We propose an analytical model for this mechanism and show the benefits stem from this approach through comprehensive experimental evaluation and comparative assessment with qualityunaware methods over real sensory multivariate contextual data.

Scalable aggregation predictive analytics

“…A comprehensive list of the available transformations can be found at Spark's website. 2 It is worth noting that transformations are not applied immediately. Instead, Spark uses a lineage graph and pipelines successive transformations to the original dataset once an action is called [32].…”

“…Platforms such as MapReduce [14], Yarn [29], Spark [32] and Mahout [22] are nowadays commonplace. Predictive modeling [26], [23] and exploratory analysis [2,3,6,20] are commonly based on statistical aggregation operators over the results of exploration queries [4,7]. Such queries involve large datasets (which may themselves be the result of linking of other different datasets) and a number of range predicates over multidimensional data vectorial representation, structured, semi-and unstructured data.…”

Machine Learning Model Updates in Edge Computing: An Optimal Stopping Theory Approach

“…Moreover, sensor systems rely on renewable energy, most widely used is solar energy, which could be slower to harvest in environments such as rain forests [19], therefore, having a power efficient device is required in order to have a well-functioning system. Another concern with communicating processed data on every sensing & reporting period is that it is possible that the data contains bias, e.g., missing or corrupted data points [4]. When disseminating them to the gateway, it can potentially be used to make inaccurate predictions of the sensed/monitored environment.…”

Knowledge-centric Analytics Queries Allocation in Edge Computing Environments

“…Analytics are derived by models dealing with dynamic optimal decisions for data deliver in light of communication efficiency [27], [6]. Several schemes exploit the computational capability of edge nodes to launch algorithms directly at the data sources [2], [9], [16].…”