3,928 research outputs found
Conversational Sensing
Recent developments in sensing technologies, mobile devices and context-aware
user interfaces have made it possible to represent information fusion and
situational awareness as a conversational process among actors - human and
machine agents - at or near the tactical edges of a network. Motivated by use
cases in the domain of security, policing and emergency response, this paper
presents an approach to information collection, fusion and sense-making based
on the use of natural language (NL) and controlled natural language (CNL) to
support richer forms of human-machine interaction. The approach uses a
conversational protocol to facilitate a flow of collaborative messages from NL
to CNL and back again in support of interactions such as: turning eyewitness
reports from human observers into actionable information (from both trained and
untrained sources); fusing information from humans and physical sensors (with
associated quality metadata); and assisting human analysts to make the best use
of available sensing assets in an area of interest (governed by management and
security policies). CNL is used as a common formal knowledge representation for
both machine and human agents to support reasoning, semantic information fusion
and generation of rationale for inferences, in ways that remain transparent to
human users. Examples are provided of various alternative styles for user
feedback, including NL, CNL and graphical feedback. A pilot experiment with
human subjects shows that a prototype conversational agent is able to gather
usable CNL information from untrained human subjects
XLab: Early Indications & Warnings from Open Source Data with Application to Biological Threat
XLab is an early warning system that addresses a broad range of national security threats using a flexible, rapidly reconfigurable architecture. XLab enables intelligence analysts to visualize, explore, and query a knowledge base constructed from multiple data sources, guided by subject matter expertise codified in threat model graphs. This paper describes a novel system prototype that addresses threats arising from biological weapons of mass destruction. The prototype applies knowledge extraction analytics-”including link estimation, entity disambiguation, and event detection-”to build a knowledge base of 40 million entities and 140 million relationships from open sources. Exact and inexact subgraph matching analytics enable analysts to search the knowledge base for instances of modeled threats. The paper introduces new methods for inexact matching that accommodate threat models with temporal and geospatial patterns. System performance is demonstrated using several simplified threat models and an embedded scenario
The MELODIES project: integrating diverse data using Linked Data and cloud computing
We present an overview of the MELODIES project, which is developing new data-intensive environmental services based on data from Earth Observation satellites, government databases, national and European agencies and more. We focus here on the capabilities and benefits of the project’s “technical platform”, which applies cloud computing and Linked Data technologies to enable the development of these services, providing flexibility and scalability
Quarry: A user-centered big data integration platform
Obtaining valuable insights and actionable knowledge from data requires cross-analysis of domain data typically coming from various sources. Doing so, inevitably imposes burdensome processes of unifying different data formats, discovering integration paths, and all this given specific analytical needs of a data analyst. Along with large volumes of data, the variety of formats, data models, and semantics drastically contribute to the complexity of such processes. Although there have been many attempts to automate various processes along the Big Data pipeline, no unified platforms accessible by users without technical skills (like statisticians or business analysts) have been proposed. In this paper, we present a Big Data integration platform (Quarry) that uses hypergraph-based metadata to facilitate (and largely automate) the integration of domain data coming from a variety of sources, and provides an intuitive interface to assist end users both in: (1) data exploration with the goal of discovering potentially relevant analysis facets, and (2) consolidation and deployment of data flows which integrate the data, and prepare them for further analysis (descriptive or predictive), visualization, and/or publishing. We validate Quarry’s functionalities with the use case of World Health Organization (WHO) epidemiologists and data analysts in their fight against Neglected Tropical Diseases (NTDs).This work is partially supported by GENESIS project, funded by the Spanish Ministerio de Ciencia, Innovación y Universidades under project TIN2016-79269-R.Peer ReviewedPostprint (author's final draft
Explaining Aggregates for Exploratory Analytics
Analysts wishing to explore multivariate data spaces,
typically pose queries involving selection operators, i.e., range
or radius queries, which define data subspaces of possible
interest and then use aggregation functions, the results of which
determine their exploratory analytics interests. However, such
aggregate query (AQ) results are simple scalars and as such,
convey limited information about the queried subspaces for
exploratory analysis.We address this shortcoming aiding analysts
to explore and understand data subspaces by contributing a novel
explanation mechanism coined XAXA: eXplaining Aggregates for
eXploratory Analytics. XAXA’s novel AQ explanations are represented
using functions obtained by a three-fold joint optimization
problem. Explanations assume the form of a set of parametric
piecewise-linear functions acquired through a statistical learning
model. A key feature of the proposed solution is that model
training is performed by only monitoring AQs and their answers
on-line. In XAXA, explanations for future AQs can be computed
without any database (DB) access and can be used to further
explore the queried data subspaces, without issuing any more
queries to the DB. We evaluate the explanation accuracy and
efficiency of XAXA through theoretically grounded metrics over
real-world and synthetic datasets and query workloads
Image mining: trends and developments
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining
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