Architecture for Analysis of Streaming Data

While several attempts have been made to construct a scalable and flexible architecture for analysis of streaming data, no general model to tackle this task exists. Thus, our goal is to build a scalable and maintainable architecture for performing analytics on streaming data. To reach this goal, we introduce a 7-layered architecture consisting of microservices and publish-subscribe software. Our study shows that this architecture yields a good balance between scalability and maintainability due to high cohesion and low coupling of the solution, as well as asynchronous communication between the layers. This architecture can help practitioners to improve their analytic solutions. It is also of interest to academics, as it is a building block for a general architecture for processing streaming data

Batch to Real-Time: Incremental Data Collection & Analytics Platform

Real-time data collection and analytics is a desirable but challenging feature to provide in data-intensive software systems. To provide highly concurrent and efficient real-time analytics on streaming data at interactive speeds requires a well-designed software architecture that makes use of a carefully selected set of software frameworks. In this paper, we report on the design and implementation of the Incremental Data Collection & Analytics Platform (IDCAP). The IDCAP provides incremental data collection and indexing in real-time of social media data; support for real-time analytics at interactive speeds; highly concurrent batch data processing supported by a novel data model; and a front-end web client that allows an analyst to manage IDCAP resources, to monitor incoming data in real-time, and to provide an interface that allows incremental queries to be performed on top of large Twitter datasets

Seer: Empowering Software Defined Networking with Data Analytics

Network complexity is increasing, making network control and orchestration a challenging task. The proliferation of network information and tools for data analytics can provide an important insight into resource provisioning and optimisation. The network knowledge incorporated in software defined networking can facilitate the knowledge driven control, leveraging the network programmability. We present Seer: a flexible, highly configurable data analytics platform for network intelligence based on software defined networking and big data principles. Seer combines a computational engine with a distributed messaging system to provide a scalable, fault tolerant and real-time platform for knowledge extraction. Our first prototype uses Apache Spark for streaming analytics and open network operating system (ONOS) controller to program a network in real-time. The first application we developed aims to predict the mobility pattern of mobile devices inside a smart city environment.Comment: 8 pages, 6 figures, Big data, data analytics, data mining, knowledge centric networking (KCN), software defined networking (SDN), Seer, 2016 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security (IUCC-CSS 2016

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study