27,531 research outputs found
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
Taming Numbers and Durations in the Model Checking Integrated Planning System
The Model Checking Integrated Planning System (MIPS) is a temporal least
commitment heuristic search planner based on a flexible object-oriented
workbench architecture. Its design clearly separates explicit and symbolic
directed exploration algorithms from the set of on-line and off-line computed
estimates and associated data structures. MIPS has shown distinguished
performance in the last two international planning competitions. In the last
event the description language was extended from pure propositional planning to
include numerical state variables, action durations, and plan quality objective
functions. Plans were no longer sequences of actions but time-stamped
schedules. As a participant of the fully automated track of the competition,
MIPS has proven to be a general system; in each track and every benchmark
domain it efficiently computed plans of remarkable quality. This article
introduces and analyzes the most important algorithmic novelties that were
necessary to tackle the new layers of expressiveness in the benchmark problems
and to achieve a high level of performance. The extensions include critical
path analysis of sequentially generated plans to generate corresponding optimal
parallel plans. The linear time algorithm to compute the parallel plan bypasses
known NP hardness results for partial ordering by scheduling plans with respect
to the set of actions and the imposed precedence relations. The efficiency of
this algorithm also allows us to improve the exploration guidance: for each
encountered planning state the corresponding approximate sequential plan is
scheduled. One major strength of MIPS is its static analysis phase that grounds
and simplifies parameterized predicates, functions and operators, that infers
knowledge to minimize the state description length, and that detects domain
object symmetries. The latter aspect is analyzed in detail. MIPS has been
developed to serve as a complete and optimal state space planner, with
admissible estimates, exploration engines and branching cuts. In the
competition version, however, certain performance compromises had to be made,
including floating point arithmetic, weighted heuristic search exploration
according to an inadmissible estimate and parameterized optimization
Physical Representation-based Predicate Optimization for a Visual Analytics Database
Querying the content of images, video, and other non-textual data sources
requires expensive content extraction methods. Modern extraction techniques are
based on deep convolutional neural networks (CNNs) and can classify objects
within images with astounding accuracy. Unfortunately, these methods are slow:
processing a single image can take about 10 milliseconds on modern GPU-based
hardware. As massive video libraries become ubiquitous, running a content-based
query over millions of video frames is prohibitive.
One promising approach to reduce the runtime cost of queries of visual
content is to use a hierarchical model, such as a cascade, where simple cases
are handled by an inexpensive classifier. Prior work has sought to design
cascades that optimize the computational cost of inference by, for example,
using smaller CNNs. However, we observe that there are critical factors besides
the inference time that dramatically impact the overall query time. Notably, by
treating the physical representation of the input image as part of our query
optimization---that is, by including image transforms, such as resolution
scaling or color-depth reduction, within the cascade---we can optimize data
handling costs and enable drastically more efficient classifier cascades.
In this paper, we propose Tahoma, which generates and evaluates many
potential classifier cascades that jointly optimize the CNN architecture and
input data representation. Our experiments on a subset of ImageNet show that
Tahoma's input transformations speed up cascades by up to 35 times. We also
find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy,
and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In
Proceedings of the 35th IEEE International Conference on Data Engineering
(ICDE 2019
Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes
This research is a survey to determine the career chosen of form four student
in commerce streams. The important aspect of the career chosen has been divided
into three, first is information about career, type of career and factor that most
influence students in choosing a career. The study was conducted at Sekolah
Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was
chosen by using non-random sampling purpose method as respondent. All
information was gather by using questionnaire. Data collected has been analyzed in
form of frequency, percentage and mean. Results are performed in table and graph.
The finding show that information about career have been improved in students
career chosen and mass media is the main factor influencing students in choosing
their career
Carbon Dynamics and Land-Use Choices: Building a Regional-Scale Multidisciplinary Model
Policy enabling tropical forests to approach their potential contribution to global-climate-change mitigation requires forecasts of land use and carbon storage on a large scale over long periods. In this paper, we present an integrated modeling methodology that addresses these needs. We model the dynamics of the human land-use system and of C pools contained in each ecosystem, as well as their interactions. The model is national scale, and is currently applied in a preliminary way to Costa Rica using data spanning a period of over fifty years. It combines an ecological process model, parameterized using field and other data, with an economic model, estimated using historical data to ensure a close link to actual behavior. These two models are linked so that ecological conditions affect land-use choices and vice versa. The integrated model predicts land use and its consequences for C storage for policy scenarios. These predictions can be used to create baselines, reward sequestration, and estimate the value in both environmental and economic terms of including C sequestration in tropical forests as part of the efforts to mitigate global climate change. The model can also be used to assess the benefits from costly activities to increase accuracy and thus reduce errors and their societal costs.carbon, sequestration, climate change, land use, modelling
Carbon Dynamics and Land-use Choices: Building a Regional-scale Multidisciplinary Model
Policy enabling tropical forests to approach their potential contribution to global-climate-change mitigation requires forecasts of land use and carbon storage on a large scale over long periods. In this paper, we present an integrated modeling methodology that addresses these needs. We model the dynamics of the human land-use system and of C pools contained in each ecosystem, as well as their interactions. The model is national scale, and is currently applied in a preliminary way to Costa Rica using data spanning a period of over fifty years. It combines an ecological process model, parameterized using field and other data, with an economic model, estimated using historical data to ensure a close link to actual behavior. These two models are linked so that ecological conditions affect land-use choices and vice versa. The integrated model predicts land use and its consequences for C storage for policy scenarios. These predictions can be used to create baselines, reward sequestration, and estimate the value in both environmental and economic terms of including C sequestration in tropical forests as part of the efforts to mitigate global climate change. The model can also be used to assess the benefits from costly activities to increase accuracy and thus reduce errors and their societal costs.carbon, sequestration, climate change, land use, modelling
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