3,849 research outputs found
Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual Phase Materials
Integrated Computational Materials Engineering (ICME) calls for the
integration of computational tools into the materials and parts development
cycle, while the Materials Genome Initiative (MGI) calls for the acceleration
of the materials development cycle through the combination of experiments,
simulation, and data. As they stand, both ICME and MGI do not prescribe how to
achieve the necessary tool integration or how to efficiently exploit the
computational tools, in combination with experiments, to accelerate the
development of new materials and materials systems. This paper addresses the
first issue by putting forward a framework for the fusion of information that
exploits correlations among sources/models and between the sources and `ground
truth'. The second issue is addressed through a multi-information source
optimization framework that identifies, given current knowledge, the next best
information source to query and where in the input space to query it via a
novel value-gradient policy. The querying decision takes into account the
ability to learn correlations between information sources, the resource cost of
querying an information source, and what a query is expected to provide in
terms of improvement over the current state. The framework is demonstrated on
the optimization of a dual-phase steel to maximize its strength-normalized
strain hardening rate. The ground truth is represented by a
microstructure-based finite element model while three low fidelity information
sources---i.e. reduced order models---based on different homogenization
assumptions---isostrain, isostress and isowork---are used to efficiently and
optimally query the materials design space.Comment: 19 pages, 11 figures, 5 table
How to Juggle Columns: An Entropy-Based Approach for Table Compression
Many relational databases exhibit complex dependencies between data attributes, caused either by the nature of the underlying data or by explicitly denormalized schemas. In data warehouse scenarios, calculated key figures may be materialized or hierarchy levels may be held within a single dimension table. Such column correlations and the resulting data redundancy may result in additional storage requirements. They may also result in bad query performance if inappropriate independence assumptions are made during query compilation. In this paper, we tackle the specific problem of detecting functional dependencies between columns to improve the compression rate for column-based database systems, which both reduces main memory consumption and improves query performance. Although a huge variety of algorithms have been proposed for detecting column dependencies in databases, we maintain that increased data volumes and recent developments in hardware architectures demand novel algorithms with much lower runtime overhead and smaller memory footprint. Our novel approach is based on entropy estimations and exploits a combination of sampling and multiple heuristics to render it applicable for a wide range of use cases. We demonstrate the quality of our approach by means of an implementation within the SAP NetWeaver Business Warehouse Accelerator. Our experiments indicate that our approach scales well with the number of columns and produces reliable dependence structure information. This both reduces memory consumption and improves performance for nontrivial queries
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
A novel Big Data analytics and intelligent technique to predict driver's intent
Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads
Filtering data based on predicates is one of the most fundamental operations
for any modern data warehouse. Techniques to accelerate the execution of filter
expressions include clustered indexes, specialized sort orders (e.g., Z-order),
multi-dimensional indexes, and, for high selectivity queries, secondary
indexes. However, these schemes are hard to tune and their performance is
inconsistent. Recent work on learned multi-dimensional indexes has introduced
the idea of automatically optimizing an index for a particular dataset and
workload. However, the performance of that work suffers in the presence of
correlated data and skewed query workloads, both of which are common in real
applications. In this paper, we introduce Tsunami, which addresses these
limitations to achieve up to 6X faster query performance and up to 8X smaller
index size than existing learned multi-dimensional indexes, in addition to up
to 11X faster query performance and 170X smaller index size than
optimally-tuned traditional indexes
Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations
Urban traffic speed prediction aims to estimate the future traffic speed for
improving the urban transportation services. Enormous efforts have been made on
exploiting spatial correlations and temporal dependencies of traffic speed
evolving patterns by leveraging explicit spatial relations (geographical
proximity) through pre-defined geographical structures ({\it e.g.}, region
grids or road networks). While achieving promising results, current traffic
speed prediction methods still suffer from ignoring implicit spatial
correlations (interactions), which cannot be captured by grid/graph
convolutions. To tackle the challenge, we propose a generic model for enabling
the current traffic speed prediction methods to preserve implicit spatial
correlations. Specifically, we first develop a Dual-Transformer architecture,
including a Spatial Transformer and a Temporal Transformer. The Spatial
Transformer automatically learns the implicit spatial correlations across the
road segments beyond the boundary of geographical structures, while the
Temporal Transformer aims to capture the dynamic changing patterns of the
implicit spatial correlations. Then, to further integrate both explicit and
implicit spatial correlations, we propose a distillation-style learning
framework, in which the existing traffic speed prediction methods are
considered as the teacher model, and the proposed Dual-Transformer
architectures are considered as the student model. The extensive experiments
over three real-world datasets indicate significant improvements of our
proposed framework over the existing methods
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