76,272 research outputs found
Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda
Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online
Control technology overview in CSI
A brief control technology overview is given in Control Structures Interaction (CSI) by illustrating that many future NASA mission present significant challenges as represented by missions having a significantly increased number of important system states which may require control and by identifying key CSI technology needs. The JPL CSI related technology developments are discussed to illustrate that some of the identified control needs are being pursued. Since experimental confirmation of the assumptions inherent in the CSI technology is critically important to establishing its readiness for space program applications, the areas of ground and flight validation require high priority
Genetic Algorithm Modeling with GPU Parallel Computing Technology
We present a multi-purpose genetic algorithm, designed and implemented with
GPGPU / CUDA parallel computing technology. The model was derived from a
multi-core CPU serial implementation, named GAME, already scientifically
successfully tested and validated on astrophysical massive data classification
problems, through a web application resource (DAMEWARE), specialized in data
mining based on Machine Learning paradigms. Since genetic algorithms are
inherently parallel, the GPGPU computing paradigm has provided an exploit of
the internal training features of the model, permitting a strong optimization
in terms of processing performances and scalability.Comment: 11 pages, 2 figures, refereed proceedings; Neural Nets and
Surroundings, Proceedings of 22nd Italian Workshop on Neural Nets, WIRN 2012;
Smart Innovation, Systems and Technologies, Vol. 19, Springe
BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations
Objective: The advent of High-Performance Computing (HPC) in recent years has
led to its increasing use in brain study through computational models. The
scale and complexity of such models are constantly increasing, leading to
challenging computational requirements. Even though modern HPC platforms can
often deal with such challenges, the vast diversity of the modeling field does
not permit for a single acceleration (or homogeneous) platform to effectively
address the complete array of modeling requirements. Approach: In this paper we
propose and build BrainFrame, a heterogeneous acceleration platform,
incorporating three distinct acceleration technologies, a Dataflow Engine, a
Xeon Phi and a GP-GPU. The PyNN framework is also integrated into the platform.
As a challenging proof of concept, we analyze the performance of BrainFrame on
different instances of a state-of-the-art neuron model, modeling the Inferior-
Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley
representation. The model instances take into account not only the neuronal-
network dimensions but also different network-connectivity circumstances that
can drastically change application workload characteristics. Main results: The
synthetic approach of three HPC technologies demonstrated that BrainFrame is
better able to cope with the modeling diversity encountered. Our performance
analysis shows clearly that the model directly affect performance and all three
technologies are required to cope with all the model use cases.Comment: 16 pages, 18 figures, 5 table
Programming MPSoC platforms: Road works ahead
This paper summarizes a special session on multicore/multi-processor system-on-chip (MPSoC) programming challenges. The current trend towards MPSoC platforms in most computing domains does not only mean a radical change in computer architecture. Even more important from a SW developer´s viewpoint, at the same time the classical sequential von Neumann programming model needs to be overcome. Efficient utilization of the MPSoC HW resources demands for radically new models and corresponding SW development tools, capable of exploiting the available parallelism and guaranteeing bug-free parallel SW. While several standards are established in the high-performance computing domain (e.g. OpenMP), it is clear that more innovations are required for successful\ud
deployment of heterogeneous embedded MPSoC. On the other hand, at least for coming years, the freedom for disruptive programming technologies is limited by the huge amount of certified sequential code that demands for a more pragmatic, gradual tool and code replacement strategy
- …