Big Data Analytics for Smart Cities: The H2020 CLASS Project

Applying big-data technologies to field applications has resulted in several new needs. First, processing data across a compute continuum spanning from cloud to edge to devices, with varying capacity, architecture etc. Second, some computations need to be made predictable (real-time response), thus supporting both data-in-motion processing and larger-scale data-at-rest processing. Last, employing an event-driven programming model that supports mixing different APIs and models, such as Map/Reduce, CEP, sequential code, etc.The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the CLASS Project (www.class-project.eu), grant agreement No. 780622.Peer ReviewedPostprint (author's final draft

SPL: An extensible language for distributed stream processing

Big data is revolutionizing how all sectors of our economy do business, including telecommunication, transportation, medical, and finance. Big data comes in two flavors: data at rest and data in motion. Processing data in motion is stream processing. Stream processing for big data analytics often requires scale that can only be delivered by a distributed system, exploiting parallelism on many hosts and many cores. One such distributed stream processing system is IBM Streams. Early customer experience with IBM Streams uncovered that another core requirement is extensibility, since customers want to build high-performance domain-specific operators for use in their streaming applications. Based on these two core requirements of distribution and extensibility, we designed and implemented the Streams Processing Language (SPL). This article describes SPL with an emphasis on the language design, distributed runtime, and extensibility mechanism. SPL is now the gateway for the IBM Streams platform, used by our customers for stream processing in a broad range of application domains. © 2017 ACM

INTELLIGENT MONITORING OF A LARGE CATAMARAN FERRY

Wave load cycles, wet-deck slamming events, accelerations and motion comfort are important considerations for high-speed catamarans operating in moderate to large waves. Although developing a hull monitoring system according to classification guidelines for such vessels is broadly acceptable, the data processing requirements for outputs such as rainflow counting, filtering, probability distribution, fatigue damage estimation and warning due to slamming can be as sophisticated to implement as the system components themselves. Advanced analytics such as machine learning and deep learning data pipelines will also create more complexities for such systems, if included. This paper provides an overview of data analytics methods and cloud computing resources for remotely monitoring motions and structural responses of a 111 m high-speed catamaran. To satisfy the data processing requirements, MATLAB Reference Architectures on Amazon Web Services (AWS) were used. Such combination enabled fast parallel computing and advanced feature engineering in a time-efficient manner. A MATLAB Production Server on AWS has been set up for near real-time analytics and execution of functions developed according to the class guidelines. A case study using Long Short‑Term Memory (LSTM) networks for ship speed and Motion Sickness Incidence (MSI) is provided and discussed. Such data architecture provides a flexible and scalable solution, leading to deeper insights through big data processing and machine learning, which supports hull monitoring functions as a service

Cyber-Physical Systems Technologies: Applications in Industry and Education

Industry 4.0 concept development forms new trends as cloud computing,  big data analysis, the industrial internet of things, machine-to-machine technologies. Cyber-physical systems (CPS) paradigm is based on these trends and integrates of computation, networking and physical processes. Synergy Center at Peter the Great St. Petersburg Polytechnic University works in the areas of intelligent systems for data processing and control, motion control systems for robotics, complex automation and mechatronics as components of CPS. Keywords: Industry 4.0, Cyber-physical systems, Digital twin; intelligent control system, automation, Global digitalisation, Practical-oriented online courses, Skills training, Joint international educational programmes

Development of HU Cloud-based Spark Applications for Streaming Data Analytics

Nowadays, streaming data overflows from various sources and technologies such as Internet of Things (IoT), making conventional data analytics methods unsuitable to manage the latency of data processing relative to the growing demand for high processing speed and algorithmically scalability [1]. Real-time streaming data analytics, which processes data while it is in motion, is required to allow many organizations to analyze streaming data effectively and efficiently for being more active in their strategies. To analyze real time “Big” streaming data, parallel and distributed computing over a cloud of computers has become a mainstream solution to allow scalability, resiliency to failure, and fast processing of massive data sets. Several open source data analytics frameworks have been proposed and developed for streaming data analytics successfully. Apache Spark is one such framework being developed at the University of California, Berkley and gains lots of attentions due to reducing IO by storing data in a memory and a unique data executing model. In Computer & Information Sciences (CISC) at Harrisburg University (HU), we have been working on building a private Cloud Computing for future research and planning to involve industry collaboration where high volumes of real time streaming data are used to develop solutions to practical problems in industry. By developing a HU Cloud based environment for Apache Spark applications for streaming data analytics with batch processing on Hadoop Distributed File System (HDFS), we can prepare future big data era that can turn big data into beneficial actions for industry needs. This research aims to develop Spark applications supporting an entire streaming data analytics workflow, which consists of data ingestion, data analytics, data visualization and data storing. In particular, we will focus on a real time stock recommender system based on state-of-the-art Machine Learning (ML)/Deep Learning (DL) frameworks such as mllib, TensorFlow, Apache mxnet and pytorch. The plan is to gather real time stock market data from Google/Yahoo finance data streams to build a model to predict a future stock market trend. The proposed Spark applications on the HU cloud-based architecture will give emphasis to finding time-series forcating module for a specific period, typically based on selected attributes. In addition, we will test scale-out architecture, efficient parallel processing and fault tolerance of Spark applications on the HU Cloud based HDFS. We believe that this research will bring the CISC program at HU significant competitive advantages globally

Knowledge Modelling and Incident Analysis for Special Cargo

The airfreight industry of shipping goods with special handling needs, also known as special cargo, suffers from nontransparent shipping processes, resulting in inefficiency. The LARA project (Lane Analysis and Route Advisor) aims at addressing these limitations and bringing innovation in special cargo route planning so as to improve operational deficiencies and customer services. In this chapter, we discuss the special cargo domain knowledge elicitation and modeling into an ontology. We also present research into cargo incidents, namely, automatic classification of incidents in free-text reports and experiments in detecting significant features associated with specific cargo incident types. Our work mainly addresses two of the main technical priority areas defined by the European Big Data Value (BDV) Strategic Research and Innovation Agenda, namely, the application of data analytics to improve data understanding and providing optimized architectures for analytics of data-at-rest and data-in-motion, the overall goal is to develop technologies contributing to the data value chain in the logistics sector. It addresses the horizontal concerns Data Analytics, Data Processing Architectures, and Data Management of the BDV Reference Model. It also addresses the vertical dimension Big Data Types and Semantics

The Issue Of Internet Polling

Surveys, polls, and focus groups are common phenomena in our daily lives. We live in a world where big data is big business. Large decisions hinge on the accuracy and predicative power of these numbers. Therefore, it should not be surprising that there is a market for the malicious manipu-1ation of data. Extreme care must be taken in the collection, checking, and processing of data to prevent decisions from being made on incorrect as­sumptions. In order to demonstrate the full potential and possible impact of these attacks, I shall provide the following example: John Doe is a member of the United States Senate. In recent years, the political pressure to make a preemptive strike against a potential nu­clear threat has grown exponentially. In some of the more extreme cases, several senators have begun asking for support to make a motion to the President for military intervention. Eventually, Senator Doe is asked to sign a petition for their cause. Senator Doe decides that he must take the concerns, priorities, and beliefs of the voters in his state into account be­fore he can make a decision as their representative

Deformation analysis of a metropolis from C- to X-band PSI: proof-of-concept with Cosmo-Skymed over Rome, Italy

Stability of monuments and subsidence of residential quarters in Rome (Italy) are depicted based on geospatial analysis of more than 310,000 Persistent Scatterers (PS) obtained from Stanford Method for Persistent Scatterers (StaMPS) processing of 32 COSMO-SkyMed 3m-resolution HH StripMap ascending mode scenes acquired between 21 March 2011 and 10 June 2013. COSMO-SkyMed PS densities and associated displacement velocities are compared with almost 20 years of historical C-band ERS- 1/2, ENVISAT and RADARSAT-1/2 imagery. Accounting for differences in image processing algorithms and satellite acquisition geometries, we assess the feasibility of ground motion monitoring in big cities and metropolitan areas by coupling newly acquired and legacy SAR in full time series. Limitations and operational benefits of the transition from medium resolution C-band to high resolution X-band PS data are discussed, alongside the potential impact on the management of expanding urban environments

Investigating How Speech And Animation Realism Influence The Perceived Personality Of Virtual Characters And Agents

The portrayed personality of virtual characters and agents is understood to influence how we perceive and engage with digital applications. Understanding how the features of speech and animation drive portrayed personality allows us to intentionally design characters to be more personalized and engaging. In this study, we use performance capture data of unscripted conversations from a variety of actors to explore the perceptual outcomes associated with the modalities of speech and motion. Specifically, we contrast full performance-driven characters to those portrayed by generated gestures and synthesized speech, analysing how the features of each influence portrayed personality according to the Big Five personality traits. We find that processing speech and motion can have mixed effects on such traits, with our results highlighting motion as the dominant modality for portraying extraversion and speech as dominant for communicating agreeableness and emotional stability. Our results can support the Extended Reality (XR) community in development of virtual characters, social agents and 3D User Interface (3DUI) agents portraying a range of targeted personalities