Can Temperature be Used as a Predictor of Data Traffic? A Real Network Big Data Analysis

The proliferation of mobile devices and big data has made it possible to understand the human movements and forecasts of precise and intelligent short and long-term data consumption of services like call, sms, or internet data which has interesting and promising applications in modern cellular networks. Human nature and moods are known to be synonymous with the physical attributes of mother nature such as temperature. The change in those physical features affects the human routines and activities such as cellular data consumptions. The future of telecommunication lies in the exploration of heap of information and data available to companies and inferring the valuable results through extensive analysis. In this paper, we analyze three main traits of cellular activity: sms, call, and internet. This paper investigates whether the relationship between the temperature and the cellular data consumption exits or not. This work introduces a novel approach to identify the strength of relationship between the temperature and cellular activity (sms, call, internet) and discuss the methods to quantify the relationship using correlation method. The real network CDR big data set - Milano Grid data set is used to analyze the behavior of the cellular activity with respect to temperature

Large Graph Analysis in the GMine System

Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges. To take advantage of such graphs, one must be able to find patterns, outliers and communities. These tasks are better performed in an interactive environment, where human expertise can guide the process. For large graphs, though, there are some challenges: the excessive processing requirements are prohibitive, and drawing hundred-thousand nodes results in cluttered images hard to comprehend. To cope with these problems, we propose an innovative framework suited for any kind of tree-like graph visual design. GMine integrates (a) a representation for graphs organized as hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b) a graph summarization methodology - CEPS. Our graph representation deals with the problem of tracing the connection aspects of a graph hierarchy with sub linear complexity, allowing one to grasp the neighborhood of a single node or of a group of nodes in a single click. As a proof of concept, the visual environment of GMine is instantiated as a system in which large graphs can be investigated globally and locally

Application of Evolutionary Network Concept in Structuring Mathematics Curriculum

Phylogenetic tree and in general, evolutionary network, has found its application well beyond the biological fields and has even percolated into recent high demanding areas, such as data mining and social media chain reactions. An extensive survey of its current applications are presented here. An attempt has been made to apply the very concept in the mathematics course curriculum inside a degree program. Various features of the tree structure are identified within the curriculum network. To highlight various key components and to enhance the visual effect, several diagrams are presented. The combined effect of these diagram provides a sense of the entire curriculum tree structure. The current study can be used as a potential tool for effective student advisement, student placement within the curriculum, efficient resource allocation, etc. Future work may encompass detailing and implementing these applications

Resembling Population Density Distribution with Massive Mobile Phone Data

As the mobile phone data (CDR data) has gained an increasing interest in research, such as social science, transportation, urban informatics, and big data, this study aims at examining the representativeness of the CDR data in terms of resemblance of the actual population density distribution from three perspectives; operator’s market share, urban-rural user population ratio, and user gender ratio. The results reveal that the representativeness of the data does not scale at the same rate with the operator’s market share, the urban-rural user population ratio of 80:20 can best represent the population density distribution, and an equal mixture of male and female user population can best resemble the population density distribution. This study is the first investigation into the representativeness of the CDR data. The findings provide useful information, which can serve an insightful guideline when dealing with the CDR data

Dealing with crosstalk in electromagnetic field measurements of portable devices

Portable devices measuring radiofrequency electromagnetic fields (RF-EMF) are affected by crosstalk: signals originating in one frequency band that are unintentionally registered in another. If this is not corrected, total exposure to RF-EMF is biased, particularly affecting closely spaced frequency bands such as GSM 1800 downlink (1,805-1,880 MHz), DECT (1,880-1,900 MHz), and UMTS uplink (1,920-1,980 MHz). This study presents an approach to detect and correct crosstalk in RF-EMF measurements, taking into account the real-life setting in which crosstalk is intermittently present, depending on the exact frequency of the signal. Personal measurements from 115 volunteers from Zurich canton, Switzerland were analyzed. Crosstalk-affected observations were identified by correlation analysis, and replaced by the median value of the unaffected observations, measured during the same activity. DECT is frequently a victim of crosstalk, and an average of 43% of observations was corrected, resulting in an average exposure reduction of 38%. GSM 1800 downlink and UMTS uplink were less often corrected (6.9% and 8.9%), resulting in minor reductions in exposure (7.1% and 0.92%). The contribution of DECT to total RF-EMF exposure is typically already low (3.2%), but is further reduced after correction (3.0%). Crosstalk corrections reduced the total exposure by 1.0% on average. Some individuals had a larger reduction of up to 16%. The code developed to make the corrections is provided for free as an R function which is easily applied to any time series of EMF measurements. Bioelectromagnetics. 39:529-538, 2018. © 2018 Wiley Periodicals, Inc