915 research outputs found
Expanding sensor networks to automate knowledge acquisition
The availability of accurate, low-cost sensors to scientists has resulted in widespread deployment in a variety of sporting and health environments. The sensor data output is often in a raw, proprietary or unstructured format. As a result, it is often difficult to query multiple sensors for complex properties or actions. In our research, we deploy a heterogeneous sensor network to detect the various biological and physiological properties in athletes during training activities. The goal for exercise physiologists is to quickly identify key intervals in exercise such as moments of stress or fatigue. This is not currently possible because of low level sensors and a lack of query language support. Thus, our motivation is to expand the sensor network with a contextual layer that enriches raw sensor data, so that it can be exploited by a high level query language. To achieve this, the domain expert specifies events in a tradiational event-condition-action format to deliver the required contextual enrichment
Crowd Sourced Semantic Enrichment (CroSSE) for knowledge driven querying of digital resources
Today, most information sources provide factual, objective knowledge, but they fail to capture personalized contextual knowledge which could be used to enrich the available factual data and contribute to their interpretation, in the context of the knowledge of the user who queries the system. This would require a knowledge framework which can accommodate both objective data and seman-
tic enrichments that capture user provided knowledge associated to the factual data in the database. Unfortunately, most conventional DBMSs lack the flexibilities necessary (a) to prevent the data and metadata, evolve quickly with changing application requirements and (b) to capture user-provided and/or crowdsourced data and knowledge for more effective decision support. In this paper, we present CrowdSourced Semantic Enrichment (CroSSE) knowledge framework which al-
lows traditional databases and semantic enrichment modules to coexist. CroSSE provides a novel Semantically Enriched SQL (SESQL) language to enrich SQL queries with information from a knowledge base containing semantic annotations.
We describe CroSSE and SESQL with examples taken from our SmartGround EU project
City Data Fusion: Sensor Data Fusion in the Internet of Things
Internet of Things (IoT) has gained substantial attention recently and play a
significant role in smart city application deployments. A number of such smart
city applications depend on sensor fusion capabilities in the cloud from
diverse data sources. We introduce the concept of IoT and present in detail ten
different parameters that govern our sensor data fusion evaluation framework.
We then evaluate the current state-of-the art in sensor data fusion against our
sensor data fusion framework. Our main goal is to examine and survey different
sensor data fusion research efforts based on our evaluation framework. The
major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed
Systems and Technologies (IJDST), 201
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
Transition of legacy systems to semantically enabled applications:TAO method and tools
Despite expectations being high, the industrial take-up of Semantic Web technologies in developing services and applications has been slower than expected. One of the main reasons is that many legacy systems have been developed without considering the potential of theWeb in integrating services and sharing resources.Without a systematic methodology and proper tool support, the migration from legacy systems to SemanticWeb Service-based systems can be a tedious and expensive process, which carries a significant risk of failure. There is an urgent need to provide strategies, allowing the migration of legacy systems to Semantic Web Services platforms, and also tools to support such strategies. In this paper we propose a methodology and its tool support for transitioning these applications to Semantic Web Services, which allow users to migrate their applications to Semantic Web Services platforms automatically or semi-automatically. The transition of the GATE system is used as a case study
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