Streaming MASSIF: Cascading Reasoning for Efficient Processing of IoT Data Streams

Abstract

In the Internet of Things (IoT), multiple sensors and devices are generating heterogeneous streams of data. To perform meaningful analysis over multiple of these streams, stream processing needs to support expressive reasoning capabilities to infer implicit facts and temporal reasoning to capture temporal dependencies. However, current approaches cannot perform the required reasoning expressivity while detecting time dependencies over high frequency data streams. There is still a mismatch between the complexity of processing and the rate data is produced in volatile domains. Therefore, we introduce Streaming MASSIF, a Cascading Reasoning approach performing expressive reasoning and complex event processing over high velocity streams. Cascading Reasoning is a vision that solves the problem of expressive reasoning over high frequency streams by introducing a hierarchical approach consisting of multiple layers. Each layer minimizes the processed data and increases the complexity of the data processing. Cascading Reasoning is a vision that has not been fully realized. Streaming MASSIF is a layered approach allowing IoT service to subscribe to high-level and temporal dependent concepts in volatile data streams. We show that Streaming MASSIF is able to handle high velocity streams up to hundreds of events per second, in combination with expressive reasoning and complex event processing. Streaming MASSIF realizes the Cascading Reasoning vision and is able to combine high expressive reasoning with high throughput of processing. Furthermore, we formalize semantically how the different layers in our Cascading Reasoning Approach collaborate.

Velocity on the Web

“…Our primary contribution to solve the processing problem is the Expressive Layered Fire-hose (ELF) (formerly streaming MASSIF) [9]. ELF is a stream reasoning platform designed after a renovated Cascading Stream Reasoning (cf Fig.…”

“…Possible reasoning framework that are suitable for L3 are Description Logic (DL), temporal logical, and Answer Set Programming (ASP). Our further contribution concerning L3 is Ontology-Based Event RecognitiON (OBERON) (formerly OBEP) [9,10,43], i.e., (i) is an A Domain-Specific Language that treats events as first-class objects [43]. OBERON uses two forms of reasoning to detect and compose events over Web streams, i.e., Description Logics reasoning and Complex Event Recognition.…”

A Complete Software Stack for IoT Time-Series Analysis that Combines Semantics and Machine Learning—Lessons Learned from the Dyversify Project

“…These rules can express hierarchical properties (e.g., every ventilation machine is a machine) or expert knowledge (e.g., ventilation is required when CO 2 is above 600 ppm). Semantic reasoners vary in expressivity, with more expressive reasoners typically being slower [6].…”

Distributed Continuous Home Care Provisioning through Personalized Monitoring & Treatment Planning

“…Streaming MASSIF is a cascading reasoning framework that enables to perform scalable expressive reasoning over high velocity data streams [4]. It performs further semantic reasoning on events filtered by the local C-SPARQL engines, in order to infer their severity and urgency.…”