7,819 research outputs found
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
QueryTogether: Enabling entity-centric exploration in multi-device collaborative search
Collaborative and co-located information access is becoming increasingly common. However, fairly little attention has been devoted to the design of ubiquitous computing approaches for spontaneous exploration of large information spaces enabling co-located collaboration. We investigate whether an entity-based user interface provides a solution to support co-located search on heterogeneous devices. We present the design and implementation of QueryTogether, a multi-device collaborative search tool through which entities such as people, documents, and keywords can be used to compose queries that can be shared to a public screen or specific users with easy touch enabled interaction. We conducted mixed-methods user experiments with twenty seven participants (nine groups of three people), to compare the collaborative search with QueryTogether to a baseline adopting established search and collaboration interfaces. Results show that QueryTogether led to more balanced contribution and search engagement. While the overall s-recall in search was similar, in the QueryTogether condition participants found most of the relevant results earlier in the tasks, and for more than half of the queries avoided text entry by manipulating recommended entities. The video analysis demonstrated a more consistent common ground through increased attention to the common screen, and more transitions between collaboration styles. Therefore, this provided a better fit for the spontaneity of ubiquitous scenarios. QueryTogether and the corresponding study demonstrate the importance of entity based interfaces to improve collaboration by facilitating balanced participation, flexibility of collaboration styles and social processing of search entities across conversation and devices. The findings promote a vision of collaborative search support in spontaneous and ubiquitous multi-device settings, and better linking of conversation objects to searchable entities
Towards Exascale Scientific Metadata Management
Advances in technology and computing hardware are enabling scientists from
all areas of science to produce massive amounts of data using large-scale
simulations or observational facilities. In this era of data deluge, effective
coordination between the data production and the analysis phases hinges on the
availability of metadata that describe the scientific datasets. Existing
workflow engines have been capturing a limited form of metadata to provide
provenance information about the identity and lineage of the data. However,
much of the data produced by simulations, experiments, and analyses still need
to be annotated manually in an ad hoc manner by domain scientists. Systematic
and transparent acquisition of rich metadata becomes a crucial prerequisite to
sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and
domain-agnostic metadata management infrastructure that can meet the demands of
extreme-scale science is notable by its absence.
To address this gap in scientific data management research and practice, we
present our vision for an integrated approach that (1) automatically captures
and manipulates information-rich metadata while the data is being produced or
analyzed and (2) stores metadata within each dataset to permeate
metadata-oblivious processes and to query metadata through established and
standardized data access interfaces. We motivate the need for the proposed
integrated approach using applications from plasma physics, climate modeling
and neuroscience, and then discuss research challenges and possible solutions
Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis
Exploring data requires a fast feedback loop from the analyst to the system,
with a latency below about 10 seconds because of human cognitive limitations.
When data becomes large or analysis becomes complex, sequential computations
can no longer be completed in a few seconds and data exploration is severely
hampered. This article describes a novel computation paradigm called
Progressive Computation for Data Analysis or more concisely Progressive
Analytics, that brings at the programming language level a low-latency
guarantee by performing computations in a progressive fashion. Moving this
progressive computation at the language level relieves the programmer of
exploratory data analysis systems from implementing the whole analytics
pipeline in a progressive way from scratch, streamlining the implementation of
scalable exploratory data analysis systems. This article describes the new
paradigm through a prototype implementation called ProgressiVis, and explains
the requirements it implies through examples.Comment: 10 page
User-centric Visualization of Data Provenance
The need to understand and track files (and inherently, data) in cloud computing systems is in high demand. Over the past years, the use of logs and data representation using graphs have become the main method for tracking and relating information to the cloud users. While it is still in use, tracking and relating information with âData Provenanceâ (i.e. series of chronicles and the derivation history of data on meta-data) is the new trend for cloud users. However, there is still much room for improving representation of data activities in cloud systems for end-users.
In this thesis, we propose âUVisP (User-centric Visualization of Data Provenance with Gestalt)â, a novel user-centric visualization technique for data provenance. This technique aims to facilitate the missing link between data movements in cloud computing environments and the end-usersâ uncertain queries over their filesâ security and life cycle within cloud systems.
The proof of concept for the UVisP technique integrates D3 (an open-source visualization API) with Gestaltsâ theory of perception to provide a range of user-centric visualizations. UVisP allows users to transform and visualize provenance (logs) with implicit prior knowledge of âGestaltsâ theory of perception.â We presented the initial development of the UVisP technique and our results show that the integration of Gestalt and the existence of âperceptual key(s)â in provenance visualization allows end-users to enhance their visualizing capabilities, extract useful knowledge and understand the visualizations better. This technique also enables end-users to develop certain methods and preferences when sighting different visualizations. For example, having the prior knowledge of Gestaltâs theory of perception and integrated with the types of visualizations offers the user-centric experience when using different visualizations. We also present significant future work that will help profile new user-centric visualizations for cloud users
On-line analytical processing
On-line analytical processing (OLAP) describes an approach to decision support, which aims to extract knowledge from a data warehouse, or more specifically, from data marts. Its main idea is providing navigation through data to non-expert users, so that they are able to interactively generate ad hoc queries without the intervention of IT professionals. This name was introduced in contrast to on-line transactional processing (OLTP), so that it reflected the different requirements and characteristics between these classes of uses. The concept falls in the area of business intelligence.Peer ReviewedPostprint (author's final draft
Alexandria: Extensible Framework for Rapid Exploration of Social Media
The Alexandria system under development at IBM Research provides an
extensible framework and platform for supporting a variety of big-data
analytics and visualizations. The system is currently focused on enabling rapid
exploration of text-based social media data. The system provides tools to help
with constructing "domain models" (i.e., families of keywords and extractors to
enable focus on tweets and other social media documents relevant to a project),
to rapidly extract and segment the relevant social media and its authors, to
apply further analytics (such as finding trends and anomalous terms), and
visualizing the results. The system architecture is centered around a variety
of REST-based service APIs to enable flexible orchestration of the system
capabilities; these are especially useful to support knowledge-worker driven
iterative exploration of social phenomena. The architecture also enables rapid
integration of Alexandria capabilities with other social media analytics
system, as has been demonstrated through an integration with IBM Research's
SystemG. This paper describes a prototypical usage scenario for Alexandria,
along with the architecture and key underlying analytics.Comment: 8 page
Proximal business intelligence on the semantic web
This is the post-print version of this article. The official version can be accessed from the link below - Copyright @ 2010 Springer.Ubiquitous information systems (UBIS) extend current Information System thinking to explicitly differentiate technology between devices and software components with relation to people and process. Adapting business data and management information to support specific user actions in context is an ongoing topic of research. Approaches typically focus on providing mechanisms to
improve specific information access and transcoding but not on how the information
can be accessed in a mobile, dynamic and ad-hoc manner. Although web ontology has been used to facilitate the loading of data warehouses, less research has been carried out on ontology based mobile reporting. This paper explores how business data can be modeled and accessed using the web ontology
language and then re-used to provide the invisibility of pervasive access; uncovering
more effective architectural models for adaptive information system strategies of this type. This exploratory work is guided in part by a vision of business intelligence that is highly distributed, mobile and fluid, adapting to sensory understanding of the underlying environment in which it operates. A proof-of concept mobile and ambient data access architecture is developed in order to further test the viability of such an approach. The paper concludes with an ontology engineering framework for systems of this type â named UBIS-ONTO
Search-Logger Analyzing Exploratory Search Tasks
In this paper, we focus on a speci c class of search cases: exploratory search tasks. To describe and quantify their complexity, we present a new methodology and corresponding tools to evaluate the user behavior when carrying out exploratory search tasks. These tools consist of a client called Search-Logger, and a server side database with frontend and an analysis environment. The client is a plug-in for Firefox web browsers. The assembly of the Search-Logger tools can
be used to carry out user studies for search tasks independent of a laboratory environment. It collects implicit user
information by logging a number of signi cant user events. Explicit information is gathered via user feedback in the form of questionnaires before and after each search task. We also present the results of a pilot user study. Some of our main observations are: When carrying out exploratory search tasks, classic search engines are mainly used as an entrance point to the web. Subsequently users work with several search systems in parallel, they have multiple browser tabs open and frequently use the clipboard to memorize, analyze and synthesize potentially useful data and information. Exploratory search tasks typically consist of various
sessions and can span from hours up to weeks
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