7,513 research outputs found
ON COMPLETENESS OF HISTORICAL RELATIONAL QUERY LANGUAGES
Numerous proposals for extending the relational data model to incorporate the temporal
dimension of data have appeared in the past several years. These proposals have differed
considerably in the way that the temporal dimension has been incorporated both into the
structure of the extended relations of these temporal models, and consequently into the
extended relational algebra or calculus that they define. Because of these differences it has
been difficult to compare the proposed models and to make judgments as to which of them
might in some sense be equivalent or even better. In this paper we define the notions of
temporally grouped and temporally ungrouped historical data models and propose
two notions of historical relational completeness, analogous to Codd's notion of relational
completeness, one for each type of model. We show that the temporally ungrouped
models are less powerful than the grouped models, but demonstrate a technique for extending
the ungrouped models with a grouping mechanism to capture the additional semantic
power of temporal grouping. For the ungrouped models we define three different languages,
a temporal logic, a logic with explicit reference to time, and a temporal algebra, and show
that under certain assumptions all three are equivalent in power. For the grouped models
we define a many-sorted logic with variables over ordinary values, historical values, and
times. Finally, we demonstrate the equivalence of this grouped calculus and the ungrouped
calculus extended with the proposed grouping mechanism. We believe the classification of
historical data models into grouped and ungrouped provides a useful framework for the
comparison of models in the literature, and furthermore the exposition of equivalent languages
for each type provides reasonable standards for common, and minimal, notions of
historical relational completeness.Information Systems Working Papers Serie
On Completeness of Historical Relational Query Languages
Numerous proposals for extending the relational data model to incorporate the temporal
dimension of data have appeared in the past several years. These proposals have differed
considerably in the way that the temporal dimension has been incorporated both into the
structure of the extended relations of these temporal models, and consequently into the
extended relational algebra or calculus that they define. Because of these differences it
has been difficult to compare the proposed models and to make judgments as to which of
them might in some sense be equivalent or even better. In this paper we define the notions of
temporally grouped and temporally ungrouped historical data models and propose two
notions of historical reIationa1 completeness, analogous to Codd's notion of relational
completeness, one for each type of model. We show that the temporally ungrouped models
are less expressive than the grouped models, but demonstrate a technique for extending the
ungrouped models with a grouping mechanism to capture the additional semantic power
of temporal grouping. For the ungrouped models we define three different languages, a
temporal logic, a logic with explicit reference to time, and a temporal algebra, and show
that under certain assumptions all three are equivalent in power. For the grouped models
we define a many-sorted logic with variables over ordinary values, historical values, and
times. Finally, we demonstrate the equivalence of this grouped calculus and the ungrouped
calculus extended with a grouping mechanism. We believe the classification of historical
data models into grouped and ungrouped provides a useful framework for the comparison
of models in the literature, and furthermore the exposition of equivalent languages for each
type provides reasonable standards for common, and minimal, notions of historical relational
completeness.Information Systems Working Papers Serie
A TEMPORAL RELATIONAL ALGEBRA AS A BASIS FOR TEMPORAL RELATIONAL COMPLETENESS
We define a temporal algebra that is applicable to any
temporal relational data model supporting discrete linear
bounded time. This algebra has the five basic
relational algebra operators extended to the temporal
domain and an operator of linear recursion. We
show that this algebra has the expressive power of a
safe temporal calculus based on the predicate temporal
logic with the until and since temporal operators.
In [CrC189], a historical calculus was proposed as a
basis for historical relational completeness. We propose
the temporal algebra defined in this paper and
the equivalent temporal calculus as an alternative basis
for temporal relational completeness.Information Systems Working Papers Serie
Survey over Existing Query and Transformation Languages
A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability
of many current Semantic Web approaches to cope with data available in such diverging
representation formalisms as XML, RDF, or Topic Maps. A common query language is the first
step to allow transparent access to data in any of these formats. To further the understanding
of the requirements and approaches proposed for query languages in the conventional as well
as the Semantic Web, this report surveys a large number of query languages for accessing
XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from
all these areas. From the detailed survey of these query languages, a common classification
scheme is derived that is useful for understanding and differentiating languages within and
among all three areas
Social media analytics: a survey of techniques, tools and platforms
This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing
The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing: Extended Survey
Graph processing is becoming increasingly prevalent across many application
domains. In spite of this prevalence, there is little research about how graphs
are actually used in practice. We performed an extensive study that consisted
of an online survey of 89 users, a review of the mailing lists, source
repositories, and whitepapers of a large suite of graph software products, and
in-person interviews with 6 users and 2 developers of these products. Our
online survey aimed at understanding: (i) the types of graphs users have; (ii)
the graph computations users run; (iii) the types of graph software users use;
and (iv) the major challenges users face when processing their graphs. We
describe the participants' responses to our questions highlighting common
patterns and challenges. Based on our interviews and survey of the rest of our
sources, we were able to answer some new questions that were raised by
participants' responses to our online survey and understand the specific
applications that use graph data and software. Our study revealed surprising
facts about graph processing in practice. In particular, real-world graphs
represent a very diverse range of entities and are often very large,
scalability and visualization are undeniably the most pressing challenges faced
by participants, and data integration, recommendations, and fraud detection are
very popular applications supported by existing graph software. We hope these
findings can guide future research
Portinari: A Data Exploration Tool to Personalize Cervical Cancer Screening
Socio-technical systems play an important role in public health screening
programs to prevent cancer. Cervical cancer incidence has significantly
decreased in countries that developed systems for organized screening engaging
medical practitioners, laboratories and patients. The system automatically
identifies individuals at risk of developing the disease and invites them for a
screening exam or a follow-up exam conducted by medical professionals. A triage
algorithm in the system aims to reduce unnecessary screening exams for
individuals at low-risk while detecting and treating individuals at high-risk.
Despite the general success of screening, the triage algorithm is a
one-size-fits all approach that is not personalized to a patient. This can
easily be observed in historical data from screening exams. Often patients rely
on personal factors to determine that they are either at high risk or not at
risk at all and take action at their own discretion. Can exploring patient
trajectories help hypothesize personal factors leading to their decisions? We
present Portinari, a data exploration tool to query and visualize future
trajectories of patients who have undergone a specific sequence of screening
exams. The web-based tool contains (a) a visual query interface (b) a backend
graph database of events in patients' lives (c) trajectory visualization using
sankey diagrams. We use Portinari to explore diverse trajectories of patients
following the Norwegian triage algorithm. The trajectories demonstrated
variable degrees of adherence to the triage algorithm and allowed
epidemiologists to hypothesize about the possible causes.Comment: Conference paper published at ICSE 2017 Buenos Aires, at the Software
Engineering in Society Track. 10 pages, 5 figure
A unified view of data-intensive flows in business intelligence systems : a survey
Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft
Penghasilan dan penilaian video pembelajaran (CD) bagi mata pelajaran Prinsip Ekonomi (BPA 1013) bertajuk permintaan dan penawaran di KUITTHO
Kajian ini dijaiankan untuk meniiai keberkesanan sebuah video pembeiajaran
(CD) mata peiajaran Prinsip Ekonomi (BPA 1013) bertajuk Permintaan dan Penawaran.
Bagi tujuan tersebut, sebuah video pembelajaran telah dihasilkan membantu pelajar
bagi memahami mata pelajaran berkenan semasa proses pengajaran dan pembelajaran
berlaku. Video pembelajaran yang dihasilkan ini kemudian dinilai dari aspek proses
pengajaran dan pembelajaran, minat dan persepsi responden terhadap ciri-ciri video
(audio dan visual). Seramai 60 orang pelajar semester 2 Sarjana Muda Sains
Pengurusan di Kolej Universiti Teknologi Tun Hussein Onn telah dipiih bagi membuat
penilaian kebolehgunaan produk ini sebagai alat bantuan mengajar di dalam kelas.
Semua data yang diperolehi kemudiannya dikumpulkan bagi dianalisis dengan
menggunakan perisian "SrarMfKM/ Pac/rageybr Rocaj/ Sb/'eace " (SPSS). Hasil dapatan
kajian yang dilakukan jelas menunjukkan video pengajaran yang dihasilkan dan dinilai
ini amat sesuai digunakan bagi tujuan memenuhi keperluan proses pengajaran dan
pembelajaran subjek ini di dalam kelas
Quality-aware model-driven service engineering
Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects
ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box
character of services
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