2,143 research outputs found
3D mapping of young stars in the solar neighbourhood with Gaia DR2
We study the three dimensional arrangement of young stars in the solar
neighbourhood using the second release of the Gaia mission (Gaia DR2) and we
provide a new, original view of the spatial configuration of the star forming
regions within 500 pc from the Sun. By smoothing the star distribution through
a gaussian filter, we construct three dimensional density maps for early-type
stars (upper-main sequence, UMS) and pre-main sequence (PMS) sources. The PMS
and the UMS samples are selected through a combination of photometric and
astrometric criteria. A side product of the analysis is a three dimensional,
G-band extinction map, which we use to correct our colour-magnitude diagram for
extinction and reddening. Both density maps show three prominent structures,
Scorpius-Centaurus, Orion, and Vela. The PMS map shows a plethora of lower mass
star forming regions, such as Taurus, Perseus, Cepheus, Cassiopeia, and
Lacerta, which are less visible in the UMS map, due to the lack of large
numbers of bright, early-type stars. We report the finding of a candidate new
open cluster towards , which could be
related to the Orion star forming complex. We estimate ages for the PMS sample
and we study the distribution of PMS stars as a function of their age. We find
that younger stars cluster in dense, compact clumps, and are surrounded by
older sources, whose distribution is instead more diffuse. The youngest groups
that we find are mainly located in Scorpius-Centaurus, Orion, Vela, and Taurus.
Cepheus, Cassiopeia, and Lacerta are instead more evolved and less numerous.
Finally, we find that the three dimensional density maps show no evidence for
the existence of the ring-like structure which is usually referred to as the
Gould Belt.Comment: 17 pages, 17 figures, 6 appendixes; accepted for publication in A&A;
image quality decreased to comply with the arXiv.org rules on file siz
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Asynchronous data retrieval from an object-oriented database
We present an object-oriented semantic database model which, similar to other object-oriented systems, combines the virtues of four concepts: the functional data model, a property inheritance hierarchy, abstract data types and message-driven computation. The main emphasis is on the last of these four concepts. We describe generic procedures that permit queries to be processed in a purely message-driven manner. A database is represented as a network of nodes and directed arcs, in which each node is a logical processing element, capable of communicating with other nodes by exchanging messages. This eliminates the need for shared memory and for centralized control during query processing. Hence, the model is suitable for implementation on a multiprocessor computer architecture, consisting of large numbers of loosely coupled processing elements
DeCaf: Diagnosing and Triaging Performance Issues in Large-Scale Cloud Services
Large scale cloud services use Key Performance Indicators (KPIs) for tracking
and monitoring performance. They usually have Service Level Objectives (SLOs)
baked into the customer agreements which are tied to these KPIs. Dependency
failures, code bugs, infrastructure failures, and other problems can cause
performance regressions. It is critical to minimize the time and manual effort
in diagnosing and triaging such issues to reduce customer impact. Large volume
of logs and mixed type of attributes (categorical, continuous) in the logs
makes diagnosis of regressions non-trivial.
In this paper, we present the design, implementation and experience from
building and deploying DeCaf, a system for automated diagnosis and triaging of
KPI issues using service logs. It uses machine learning along with pattern
mining to help service owners automatically root cause and triage performance
issues. We present the learnings and results from case studies on two large
scale cloud services in Microsoft where DeCaf successfully diagnosed 10 known
and 31 unknown issues. DeCaf also automatically triages the identified issues
by leveraging historical data. Our key insights are that for any such diagnosis
tool to be effective in practice, it should a) scale to large volumes of
service logs and attributes, b) support different types of KPIs and ranking
functions, c) be integrated into the DevOps processes.Comment: To be published in the proceedings of ICSE-SEIP '20, Seoul, Republic
of Kore
Towards Data Sharing across Decentralized and Federated IoT Data Analytics Platforms
In the past decade the Internet-of-Things concept has overwhelmingly entered all of the fields where data are produced and processed, thus, resulting in a plethora of IoT platforms, typically cloud-based, that centralize data and services management. In this scenario, the development of IoT services in domains such as smart cities, smart industry, e-health, automotive, are possible only for the owner of the IoT deployments or for ad-hoc business one-to-one collaboration agreements. The realization of "smarter" IoT services or even services that are not viable today envisions a complete data sharing with the usage of multiple data sources from multiple parties and the interconnection with other IoT services.
In this context, this work studies several aspects of data sharing focusing on Internet-of-Things. We work towards the hyperconnection of IoT services to analyze data that goes beyond the boundaries of a single IoT system. This thesis presents a data analytics platform that: i) treats data analytics processes as services and decouples their management from the data analytics development; ii) decentralizes the data management and the execution of data analytics services between fog, edge and cloud; iii) federates peers of data analytics platforms managed by multiple parties allowing the design to scale into federation of federations; iv) encompasses intelligent handling of security and data usage control across the federation of decentralized platforms instances to reduce data and service management complexity.
The proposed solution is experimentally evaluated in terms of performances and validated against use cases. Further, this work adopts and extends available standards and open sources, after an analysis of their capabilities, fostering an easier acceptance of the proposed framework. We also report efforts to initiate an IoT services ecosystem among 27 cities in Europe and Korea based on a novel methodology.
We believe that this thesis open a viable path towards a hyperconnection of IoT data and services, minimizing the human effort to manage it, but leaving the full control of the data and service management to the users' will
A Semantic-Agent Framework for PaaS Interoperability
Suchismita Hoare, Na Helian, and Nathan Baddoo, 'A Semantic-Agent Framework for PaaS Interoperability', in Proceedings of the The IEEE International Conference on Cloud and Big Data Computing, Toulouse, France, 18-21, July 2016. DOI: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0126 © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cloud Platform as a Service (PaaS) is poised for a wider adoption by its relevant stakeholders, especially Cloud application developers. Despite this, the service model is still plagued with several adoption inhibitors, one of which is lack of interoperability between proprietary application infrastructure services of public PaaS solutions. Although there is some progress in addressing the general PaaS interoperability issue through various devised solutions focused primarily on API compatibility and platform-agnostic application design models, interoperability specific to differentiated services provided by the existing public PaaS providers and the resultant disparity owing to the offered services’ semantics has not been addressed effectively, yet. The literature indicates that this dimension of PaaS interoperability is awaiting evolution in the state-of-the-art. This paper proposes the initial system design of a PaaS interoperability (IntPaaS) framework to be developed through the integration of semantic and agent technologies to enable transparent interoperability between incompatible PaaS services. This will involve uniform description through semantic annotation of PaaS provider services utilizing the OWL-S ontology, creating a knowledgebase that enables software agents to automatically search for suitable services to support Cloud-based Greenfield application development. The rest of the paper discusses the identified research problem along with the proposed solution to address the issue.Submitted Versio
Mapping young stellar populations towards Orion with Gaia DR1
We use the first data release of the Gaia mission to explore the three
dimensional arrangement and the age ordering of the many stellar groups towards
the Orion OB association, aiming at a new classification and characterization
of the stellar population. We make use of the parallaxes and proper motions
provided in the Tycho Gaia Astrometric Solution (TGAS) sub-set of the Gaia
catalogue, and of the combination of Gaia and 2MASS photometry. In TGAS we find
evidence for the presence of a young population, at a parallax , loosely distributed around some known clusters: 25 Ori,
Ori and Ori, and NGC 1980 ( Ori). The low mass
counterpart of this population is visible in the color-magnitude diagrams
constructed by combining Gaia and 2MASS photometry. We study the density
distribution of the young sources in the sky. We find the same groups as in
TGAS, and also some other density enhancements that might be related to the
recently discovered Orion X group, the Orion dust ring, and to the
Ori complex. We estimate the ages of this population and we infer the presence
of an age gradient going from 25 Ori (13-15 Myr) to the ONC (1-2 Myr). We
confirm this age ordering by repeating the Bayesian fit using the Pan-STARRS1
data. The estimated ages towards the NGC 1980 cluster span a broad range of
values. This can either be due to the presence of two populations coming from
two different episodes of star formation or to a large spread along the line of
sight of the same population. Our results form the first step towards using the
Gaia data to unravel the complex star formation history of the Orion region in
terms of the different star formation episodes, their duration, and their
effects on the surrounding interstellar medium.Comment: 17 pages, 17 figure
From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey
Context data is in demand more than ever with the rapid increase in the
development of many context-aware Internet of Things applications. Research in
context and context-awareness is being conducted to broaden its applicability
in light of many practical and technical challenges. One of the challenges is
improving performance when responding to large number of context queries.
Context Management Platforms that infer and deliver context to applications
measure this problem using Quality of Service (QoS) parameters. Although
caching is a proven way to improve QoS, transiency of context and features such
as variability, heterogeneity of context queries pose an additional real-time
cost management problem. This paper presents a critical survey of
state-of-the-art in adaptive data caching with the objective of developing a
body of knowledge in cost- and performance-efficient adaptive caching
strategies. We comprehensively survey a large number of research publications
and evaluate, compare, and contrast different techniques, policies, approaches,
and schemes in adaptive caching. Our critical analysis is motivated by the
focus on adaptively caching context as a core research problem. A formal
definition for adaptive context caching is then proposed, followed by
identified features and requirements of a well-designed, objective optimal
adaptive context caching strategy.Comment: This paper is currently under review with ACM Computing Surveys
Journal at this time of publishing in arxiv.or
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