63,747 research outputs found
Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams
Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems
(CPS) present novel challenges to Big Data platforms for performing online
analytics. Ubiquitous sensors from IoT deployments are able to generate data
streams at high velocity, that include information from a variety of domains,
and accumulate to large volumes on disk. Complex Event Processing (CEP) is
recognized as an important real-time computing paradigm for analyzing
continuous data streams. However, existing work on CEP is largely limited to
relational query processing, exposing two distinctive gaps for query
specification and execution: (1) infusing the relational query model with
higher level knowledge semantics, and (2) seamless query evaluation across
temporal spaces that span past, present and future events. These allow
accessible analytics over data streams having properties from different
disciplines, and help span the velocity (real-time) and volume (persistent)
dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP)
framework that provides domain-aware knowledge query constructs along with
temporal operators that allow end-to-end queries to span across real-time and
persistent streams. We translate this query model to efficient query execution
over online and offline data streams, proposing several optimizations to
mitigate the overheads introduced by evaluating semantic predicates and in
accessing high-volume historic data streams. The proposed X-CEP query model and
execution approaches are implemented in our prototype semantic CEP engine,
SCEPter. We validate our query model using domain-aware CEP queries from a
real-world Smart Power Grid application, and experimentally analyze the
benefits of our optimizations for executing these queries, using event streams
from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems,
October 27, 201
spChains: A Declarative Framework for Data Stream Processing in Pervasive Applications
Pervasive applications rely on increasingly complex streams of sensor data continuously captured from the physical world. Such data is crucial to enable applications to ``understand'' the current context and to infer the right actions to perform, be they fully automatic or involving some user decisions. However, the continuous nature of such streams, the relatively high throughput at which data is generated and the number of sensors usually deployed in the environment, make direct data handling practically unfeasible. Data not only needs to be cleaned, but it must also be filtered and aggregated to relieve higher level algorithms from near real-time handling of such massive data flows. We propose here a stream-processing framework (spChains), based upon state-of-the-art stream processing engines, which enables declarative and modular composition of stream processing chains built atop of a set of extensible stream processing blocks. While stream processing blocks are delivered as a standard, yet extensible, library of application-independent processing elements, chains can be defined by the pervasive application engineering team. We demonstrate the flexibility and effectiveness of the spChains framework on two real-world applications in the energy management and in the industrial plant management domains, by evaluating them on a prototype implementation based on the Esper stream processo
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
Outlier detection techniques for wireless sensor networks: A survey
In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree
Fast Search for Dynamic Multi-Relational Graphs
Acting on time-critical events by processing ever growing social media or
news streams is a major technical challenge. Many of these data sources can be
modeled as multi-relational graphs. Continuous queries or techniques to search
for rare events that typically arise in monitoring applications have been
studied extensively for relational databases. This work is dedicated to answer
the question that emerges naturally: how can we efficiently execute a
continuous query on a dynamic graph? This paper presents an exact subgraph
search algorithm that exploits the temporal characteristics of representative
queries for online news or social media monitoring. The algorithm is based on a
novel data structure called the Subgraph Join Tree (SJ-Tree) that leverages the
structural and semantic characteristics of the underlying multi-relational
graph. The paper concludes with extensive experimentation on several real-world
datasets that demonstrates the validity of this approach.Comment: SIGMOD Workshop on Dynamic Networks Management and Mining (DyNetMM),
201
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