11,425 research outputs found
Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services
Presently, a very large number of public and private data sets are available
from local governments. In most cases, they are not semantically interoperable
and a huge human effort would be needed to create integrated ontologies and
knowledge base for smart city. Smart City ontology is not yet standardized, and
a lot of research work is needed to identify models that can easily support the
data reconciliation, the management of the complexity, to allow the data
reasoning. In this paper, a system for data ingestion and reconciliation of
smart cities related aspects as road graph, services available on the roads,
traffic sensors etc., is proposed. The system allows managing a big data volume
of data coming from a variety of sources considering both static and dynamic
data. These data are mapped to a smart-city ontology, called KM4City (Knowledge
Model for City), and stored into an RDF-Store where they are available for
applications via SPARQL queries to provide new services to the users via
specific applications of public administration and enterprises. The paper
presents the process adopted to produce the ontology and the big data
architecture for the knowledge base feeding on the basis of open and private
data, and the mechanisms adopted for the data verification, reconciliation and
validation. Some examples about the possible usage of the coherent big data
knowledge base produced are also offered and are accessible from the RDF-Store
and related services. The article also presented the work performed about
reconciliation algorithms and their comparative assessment and selection
A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets
The term "outlier" can generally be defined as an observation that is significantly different from
the other values in a data set. The outliers may be instances of error or indicate events. The
task of outlier detection aims at identifying such outliers in order to improve the analysis of
data and further discover interesting and useful knowledge about unusual events within numerous
applications domains. In this paper, we report on contemporary unsupervised outlier detection
techniques for multiple types of data sets and provide a comprehensive taxonomy framework and
two decision trees to select the most suitable technique based on data set. Furthermore, we
highlight the advantages, disadvantages and performance issues of each class of outlier detection
techniques under this taxonomy framework
Human-Object Interaction Detection:A Quick Survey and Examination of Methods
Human-object interaction detection is a relatively new task in the world of
computer vision and visual semantic information extraction. With the goal of
machines identifying interactions that humans perform on objects, there are
many real-world use cases for the research in this field. To our knowledge,
this is the first general survey of the state-of-the-art and milestone works in
this field. We provide a basic survey of the developments in the field of
human-object interaction detection. Many works in this field use multi-stream
convolutional neural network architectures, which combine features from
multiple sources in the input image. Most commonly these are the humans and
objects in question, as well as the spatial quality of the two. As far as we
are aware, there have not been in-depth studies performed that look into the
performance of each component individually. In order to provide insight to
future researchers, we perform an individualized study that examines the
performance of each component of a multi-stream convolutional neural network
architecture for human-object interaction detection. Specifically, we examine
the HORCNN architecture as it is a foundational work in the field. In addition,
we provide an in-depth look at the HICO-DET dataset, a popular benchmark in the
field of human-object interaction detection. Code and papers can be found at
https://github.com/SHI-Labs/Human-Object-Interaction-Detection.Comment: Published at The 1st International Workshop On Human-Centric
Multimedia Analysis, at ACM Multimedia Conference 202
An Approach of Data Mining Techniques Using Firewall Detection for Security and Event Management System
Security is one of the most important issues to force a lot of research and development effort in last decades. We are introduced a mining technique like firewall detection and frequent item set selection to enhance the system security in event management system. In addition, we are increasing the deduction techniques we have try to overcome attackers in data mining rules using our SIEM project. In proposed work to leverages to significantly improve attack detection and mitigate attack consequences. And also we proposed approach in an advanced decision-making system that supports domain expert’s targeted events based on the individuality of the exposed IWIs. Furthermore, the application of different aggregation functions besides minimum and maximum of the item sets. Frequent and infrequent weighted item sets represent correlations frequently holding the data in which items may weight differently. However, we need is discovering the rare or frequent data correlations, cost function would get minimized using data mining techniques. There are many issues discovering rare data like processing the larger data, it takes more for process. Not applicable to discovering data like minimum of certain values. We need to handle the issue of discovering rare and weighted item sets, the frequent weighted itemset (WI) mining problem. Two novel quality measures are proposed to drive the WI mining process and Minimal WI mining efficiently in SIEM system
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