Investigating into the Prevalence of Complex Event Processing and Predictive Analytics in the Transportation and Logistics Sector: Initial Findings From Scientific Literature

As ever new sensor solutions are invading people’s everyday lives and business processes, the use of the signals and events provided by the devices poses a challenge. Innovative ways of handling the large amount of data promise an effective and efficient means to overcome that challenge. With the help of complex event processing and predictive techniques, added value can be created. While complex event processing is able to process the multitude of signals coming from the sensors in a continuous manner, predictive analytics addresses the likelihood of a certain future state or behavior by detecting patterns from the signal database and predicting the future according to the detections. As to the transportation and logistics domain, processing the signal stream and predicting the future promises a big impact on the operations because the transportation and logistics sector is known as a very complex one. The complexity of the sector is linked with the many stakeholders taking part in a variety of operations and the partly high level of automation often being accompanied by manual processes. Hence, predictions help to prepare better for upcoming situations and challenges and, thus, to save resources and cost. The present paper is to investigate the prevalence of complex event processing and predictive analytics in logistics and transportation cases in the research literature in order to motivate a subsequent systematic literature view as the next step in the research endeavor

Detecting Irregular Patterns in IoT Streaming Data for Fall Detection

Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to enable predictive analytics for automated notification and decision support. In this work, we address the problem of learning an irregular human activity pattern, fall, from streaming IoT data from wearable sensors. We present a deep neural network model for detecting fall based on accelerometer data giving 98.75 percent accuracy using an online physical activity monitoring dataset called "MobiAct", which was published by Vavoulas et al. The initial model was developed using IBM Watson studio and then later transferred and deployed on IBM Cloud with the streaming analytics service supported by IBM Streams for monitoring real-time IoT data. We also present the systems architecture of the real-time fall detection framework that we intend to use with mbientlabs wearable health monitoring sensors for real time patient monitoring at retirement homes or rehabilitation clinics.Comment: 7 page

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

AIDA Framework: Real-Time Correlation and Prediction of Intrusion Detection Alerts

In this paper, we present AIDA, an analytical framework for processing intrusion detection alerts with a focus on alert correlation and predictive analytics. The framework contains components that filter, aggregate, and correlate the alerts, and predict future security events using the predictive rules distilled from historical records. The components are based on stream processing and use selected features of data mining (namely sequential rule mining) and complex event processing. The framework was deployed as an analytical component of an alert sharing platform, where alerts from intrusion detection systems, honeypots, and other data sources are exchanged among the community of peers. The deployment is briefly described and evaluated to illustrate the capabilities of the framework in practice. Further, the framework may be deployed locally for experimentations over datasets