With the rising amount of publicly available data, data-driven modeling is becoming increasingly popular. Geospatial data is one of the most important facets that can be combined with virtually all data science real-world applications. However, there is a lack of customized geospatial data that can be used in various data science applications from different domains, e.g., hydrology, political science, climatology, and agriculture. This paper introduces a Spark-based extractor that can extract rich geospatial datasets from OpenStreetMap (OSM). OSM hosts crowd-sourced geospatial data that represent a variety of natural and human-made features, e.g., lakes, buildings, and roads. The size of this data is extremely huge and requires complex processing before being ready to use in data science. The proposed extractor runs on Apache SparkSQL which allows it to scale to the Planet.osm file which spans the entire world. In addition to the extractor, we make the data available in various standard formats, e.g., GeoJSON, CSV, KML, and Shapefile. Furthermore, we host these datasets on UCR-Star which allows users to visually explore these datasets and download any subset of the data for any geospatial region.

This paper introduces the open-source Beast system for scalable exploratory data science on big spatio-temporal data. Beast is based on well-established research and has been released to assist the research community with analyzing big spatio-temporal data. Beast provides a set of extensible components that naturally integrate with Spark to build exploratory data science pipelines. Beast can install in less than a minute on an existing Spark cluster and provides a wide array of features including loading vector and raster data represented in standard file formats, synthetic data generation for benchmarking, load-balanced spatial partitioning, data summarization, interactive visualization, and more. Beast builds on several research projects; its goal is to make all this research widely available to researchers in one integrative and coherent system.

Rumours spreading in nowadays highly connected society is an interesting and important topic in studying human behaviour and communication networks dynamics. As rumours propagation or evolution with respect to time is highly submitted to networks users’ dynamics, rumours propagation in a targeted community may inherit self-purification mechanism due to human ability to judge and clarify allegations and assumptions not based on truth. In this article, we analysed rumours spreading through social media taking into account self-purification mechanism using epidemic control approach. Rumours spreading threshold value was obtained. Results of numerical simulation validated theoretical analysis and suggested controlling social media influence on potential rumours purifiers and disseminators.

In IP networks, packets forwarding performance can be improved by adding more nodes and dividing the network into smaller segments. Being able to measure and predict traffic flows to direct to a given segment can be crucial in respecting traffic shaping, scheduling and QoS. This paper proposes to model network packets forwarding performance for optimization and prediction purposes by using multi-layer feed-forward neural network model that uses sigmoid functions to activate the hidden nodes. Gradient descent technique has been considered to optimize and enhance the MLP accuracy. Simulations of MPL neurons training stages pointed out a relative improvement of the forwarding process when network posses a larger density of neurons. Numerical results validated our theoretical analysis and confirmed that to enhance the forwarding process, it is necessary to divide the network into small segments by optimizing resources allocation.

Mobile cloud computing (MCC) has gained much attention from both academia and industry in recent years. It can support new types of services, such as m‐commerce, m‐learning, and mobile healthcare, and enrich mobile users’ experience and satisfaction by taking full advantage of cloud computing. In MCC architectures multipath communications can be achieved with multihomed mobile devices, so as to utilize multiple paths for data transmission in parallel. They can achieve better utilization of bandwidth resource, split traffic for load balancing, and enhance reliability, fault tolerance, and robustness for applications. However, little attention has been paid to model the reliability of multipath communications in case of path failure. In this paper we investigate the reliability of concurrent multipath communications in MCC architectures and propose two reliability models when paths are failure. One is for static path failure where the failed paths cannot recover for communication in some delay time. The other is for dynamic path failure where the failed paths can recover in some delay time. Finally, numerical results are given to illustrate the reliability of multipath communications.

This paper examines two kinds of bang-bang semidigital dual loop CDR architectures: 2nd-order and 3rd-order. Quantitative analysis is made in detail between the two in the presence of spread spectrum clocking (SSC) which hasn't been well studied. Moreover, a novel SSC estimator based on 3rd-order architecture is proposed to suppress the frequency-overshoot of conventional 3rd-order CDR happening at the switching points of SSC. The depth of its SSC tracking may be up to 10000ppm at 30 KHz which helps to reduce EMI.