Query: An Efficient Industrial Big-Data Engine

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Business intelligence and analytics: From big data to big impact <=SEP=> 

Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics <=SEP=> The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.

Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing <=SEP=> We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that allows programmers to perform in-memory computations on large clusters while retaining the fault tolerance of data flow models like MapReduce. RDDs are motivated by two types of applications that current data flow systems handle inefficiently: iterative algorithms, which are common in graph applications and machine learning, and interactive data mining tools. In both cases, keeping data in memory can improve performance by an order of magnitude. To achieve fault tolerance efficiently, RDDs provide a highly restricted form of shared memory: they are read-only datasets that can only be constructed through bulk operations on other RDDs. However, we show that RDDs are expressive enough to capture a wide class of computations, including MapReduce and specialized programming models for iterative jobs such as Pregel. Our implementation of RDDs can outperform Hadoop by 20× for iterative jobs and can be used interactively to search a 1 TB dataset with latencies of 5–7 seconds.

BigBench: towards an industry standard benchmark for big data analytics <=SEP=> There is a tremendous interest in big data by academia, industry and a large user base. Several commercial and open source providers unleashed a variety of products to support big data storage and processing. As these products mature, there is a need to evaluate and compare the performance of these systems.
 In this paper, we present BigBench, an end-to-end big data benchmark proposal. The underlying business model of BigBench is a product retailer. The proposal covers a data model and synthetic data generator that addresses the variety, velocity and volume aspects of big data systems containing structured, semi-structured and unstructured data. The structured part of the BigBench data model is adopted from the TPC-DS benchmark, which is enriched with semi-structured and unstructured data components. The semi-structured part captures registered and guest user clicks on the retailer's website. The unstructured data captures product reviews submitted online. The data generator designed for BigBench provides scalable volumes of raw data based on a scale factor. The BigBench workload is designed around a set of queries against the data model. From a business prospective, the queries cover the different categories of big data analytics proposed by McKinsey. From a technical prospective, the queries are designed to span three different dimensions based on data sources, query processing types and analytic techniques.
 We illustrate the feasibility of BigBench by implementing it on the Teradata Aster Database. The test includes generating and loading a 200 Gigabyte BigBench data set and testing the workload by executing the BigBench queries (written using Teradata Aster SQL-MR) and reporting their response times.

Big data analytics in healthcare: promise and potential <=SEP=> OBJECTIVE
To describe the promise and potential of big data analytics in healthcare.


METHODS
The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions.


RESULTS
The paper provides a broad overview of big data analytics for healthcare researchers and practitioners.


CONCLUSIONS
Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome.

Local energy-based shape histogram feature extraction technique for breast cancer diagnosis <=SEP=> 25 26 27 28 29 30 31 32 33 34 35 Article history: Available online xxxx

An unusual case of predation: dog pack or cougar attack? <=SEP=> Injuries produced by animals are capable of leaving severe patterns and in some cases may result in the death of the attacked individual. Law enforcement authorities may come to erroneous conclusions about the source of the bites based on their awareness of animals present and similarities of the injuries to the untrained eye, with dreadful consequences. Expertise of a carnivore biologist and an odontologist that indentifies the particularities of bite marks may be useful for identifying the attacking species. We present the investigation of a fatal dog pack attack involving a 43-year-old man in Bell Ville (Argentina) where the evidence provided by a forensic dentist and a biologist was categorical for establishing the animal species involved. Because of the unusual characteristics of the wounds and the initial hypothesis made by local authorities of a cougar attack, habits and specific patterns of both dog pack and cougar predation on humans are discussed.

Hopping of a monopedal robot with a biarticular muscle driven by electromagnetic linear actuators <=SEP=> The compliance of muscles with external forces and the structural stability given by biarticular muscles are important features of animals to realize dynamic whole body motions such as running and hopping in various environments. For this reason, we have been studying an electromagnetic linear actuator. This actuator can emulate the behavior of a human muscle such as the spring-damper characteristics by quick control of the output force (i.e. impedance control) and it is expected to be used as an artificial muscle. In this paper, we develop a monopedal robot possessing bi- and mono-articular muscles implemented by linear actuators. Thanks to the biarticular muscle, the bouncing direction of the robot can be controlled by changing the stiffness ellipse at the endpoint (i.e. foot) of the robot. We confirm that the bouncing direction of the robot can be controlled and hopping can be achieved by changing the stiffness ellipse.

Diagnosability of discrete-event systems <=SEP=> Abstruct-Fault detection and isolation is a crucial and challenging task in the automatic control of large complex systems. We propose a discrete-event system (DES) approach to the problem of failure diagnosis. We introduce two related notions of diagnosability of DES’s in the framework of formal languages and compare diagnosability with the related notions of observability and invertibility. We present a systematic procedure for detection and isolation of failure events using diagnosers and provide necessary and sufficient conditions for a language to be diagnosable. The diagnoser performs diagnostics using online observations of the system behavior; it is also used to state and verify off-line the necessary and sufficient conditions for diagnosability. These conditions are stated on the diagnoser or variations thereof. The approach to failure diagnosis presented in this paper is applicable to systems that fall naturally in the class of DES’s; moreover, for the purpose of diagnosis, most continuous variable dynamic systems can be viewed as DES’s at a higher level of abstraction. In a companion paper [20], we provide a methodology for building DES models for the purpose of failure diagnosis and present applications of the theory developed in this paper.

A NOVEL OF RECONFIGURABLE PLANAR ANTENNA ARRAY ( RPAA ) WITH BEAM STEERING CONTROL <=SEP=> A new antenna structure is formed by combining the concept of reconfigurable planar antenna array (RPAA) with the parasitic elements to produce beam steering patterns. The antenna has been integrated with the PIN diode switches that enable the beam to be steered in the desired direction. This has been done by changing the switch state to either on or off mode. In this work, a number of parasitic elements have been applied to the antenna, namely reflectors and directors. They are placed in between the driven elements, which is aimed to improve the beam steering angle. With such configuration, the main beam radiated by the array can be tilted due to the effect of mutual coupling between the driven elements and parasitic elements (reflectors and director). The unique property of this antenna design is that instead of fabricating all together in the same plane, the antenna’s feeding network is separated from the antenna radiating elements (the patches) by an air gap distance. This allows reducing the spurious effects from the feeding line. The optimization results for the resonant Corresponding author: M. T. Ali (mizi732002@yahoo.com).

MC-HOG Correlation Tracking with Saliency Proposal <=SEP=> Designing effective feature and handling the model drift problem are two important aspects for online visual tracking. For feature representation, gradient and color features are most widely used, but how to effectively combine them for visual tracking is still an open problem. In this paper, we propose a rich feature descriptor, MC-HOG, by leveraging rich gradient information across multiple color channels or spaces. Then MC-HOG features are embedded into the correlation tracking framework to estimate the state of the target. For handling the model drift problem caused by occlusion or distracter, we propose saliency proposals as prior information to provide candidates and reduce background interference. In addition to saliency proposals, a ranking strategy is proposed to determine the importance of these proposals by exploiting the learnt appearance filter, historical preserved object samples and the distracting proposals. In this way, the proposed approach could effectively explore the color-gradient characteristics and alleviate the model drift problem. Extensive evaluations performed on the benchmark dataset show the superiority of the proposed method.

A Deep Learning Approach to Universal Skin Disease Classification <=SEP=> Skin diseases are very common in people’s daily life. Each year, millions of people in American are affected by all kinds of skin disorders. Diagnosis of skin diseases sometimes requires a high-level of expertise due to the variety of their visual aspects. As human judgment are often subjective and hardly reproducible, to achieve a more objective and reliable diagnosis, a computer aided diagnostic system should be considered. In this paper, we investigate the feasibility of constructing a universal skin disease diagnosis system using deep convolutional neural network (CNN). We train the CNN architecture using the 23,000 skin disease images from the Dermnet dataset and test its performance with both the Dermnet and OLE, another skin disease dataset, images. Our system can achieve as high as 73.1% Top-1 accuracy and 91.0% Top-5 accuracy when testing on the Dermnet dataset. For the test on the OLE dataset, Top-1 and Top-5 accuracies are 31.1% and 69.5%. We show that these accuracies can be further improved if more training images are used.

Association Rules Mining : A Recent Overview <=SEP=> In this paper, we provide the preliminaries of basic concepts about association rule mining and survey the list of existing association rule mining techniques. Of course, a single article cannot be a complete review of all the algorithms, yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions that have yet to be explored.

Efficient Discovery of Statistically Significant Association Rules <=SEP=> Searching statistically significant association rules is an important but neglected problem. Traditional association rules do not capture the idea of statistical dependence and the resulting rules can be spurious, while the most significant rules may be missing. This leads to erroneous models and predictions which often become expensive.The problem is computationally very difficult, because the significance is not a monotonic property. However, in this paper we prove several other properties, which can be used for pruning the search space. The properties are implemented in the StatApriori algorithm, which searches statistically significant, non-redundant association rules. Based on both theoretical and empirical observations, the resulting rules are very accurate compared to traditional association rules. In addition, StatApriori can work with extremely low frequencies, thus finding new interesting rules.

Network Hardware-Accelerated Consensus <=SEP=> Consensus protocols are the foundation for building many fault-tolerant distributed systems and services. This paper posits that there are significant performance benefits to be gained by offering consensus as a network service (CAANS). CAANS leverages recent advances in commodity networking hardware design and programmability to implement consensus protocol logic in network devices. CAANS provides a complete Paxos protocol, is a dropin replacement for software-based implementations of Paxos, makes no restrictions on network topologies, and is implemented in a higher-level, data-plane programming language, allowing for portability across a range of target devices. At the same time, CAANS significantly increases throughput and reduces latency for consensus operations. Consensus logic executing in hardware can transmit consensus messages at line speed, with latency only slightly higher than simply forwarding packets. Report Info

Major components of the gravity recommendation system <=SEP=> The Netflix Prize is a collaborative filtering problem. This subfield of machine learning became popular in the late 1990s with the spread of online services that used recommendation systems (e.g. Amazon, Yahoo! Music, and of course Netflix). The aim of such a system is to predict what items a user might like based on his/her and other users' previous ratings. The Netflix Prize dataset is much larger than former benchmark datasets, therefore the scalability of the algorithms is a must. This paper describes the major components of our blending based solution, called the Gravity Recommendation System (GRS). In the Netflix Prize contest, it attained RMSE 0.8743 as of November 2007. We now compare the effectiveness of some selected individual and combined approaches on a particular subset of the Prize dataset, and discuss their important features and drawbacks.

What's Your Next Move: User Activity Prediction in Location-based Social Networks <=SEP=> Location-based social networks have been gaining increasing popularity in recent years. To increase users’ engagement with location-based services, it is important to provide attractive features, one of which is geo-targeted ads and coupons. To make ads and coupon delivery more effective, it is essential to predict the location that is most likely to be visited by a user at the next step. However, an inherent challenge in location prediction is a huge prediction space, with millions of distinct check-in locations as prediction target. In this paper we exploit the check-in category information to model the underlying user movement pattern. We propose a framework which uses a mixed hidden Markov model to predict the category of user activity at the next step and then predict the most likely location given the estimated category distribution. The advantages of modeling the category level include a significantly reduced prediction space and a precise expression of the semantic meaning of user activities. Extensive experimental results show that, with the predicted category distribution, the number of location candidates for prediction is 5.45 times smaller, while the prediction accuracy is 13.21% higher.

Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning <=SEP=> We develop a parameter-free face recognition algorithm which is insensitive to large variations in lighting, expression, occlusion, and age using a single gallery sample per subject. We take advantage of the observation that equidistant prototypes embedding is an optimal embedding that maximizes the minimum one-against-the-rest margin between the classes. Rather than preserving the global or local structure of the training data, our method, called linear regression analysis (LRA), applies least-square regression technique to map gallery samples to the equally distant locations, regardless of the true structure of training data. Further, a novel generic learning method, which maps the intra-class facial differences of the generic faces to the zero vectors, is incorporated to enhance the generalization capability of LRA. Using this novel method, learning based on only a handful of generic classes can largely improve the face recognition performance, even when the generic data are collected from a different database and camera set-up. The incremental learning based on the Greville algorithm makes the mapping matrix efficiently updated from the newly coming gallery classes, training samples, or generic variations. Although it is fairly simple and parameter-free, LRA, combined with commonly used local descriptors, such as Gabor representation and local binary patterns, outperforms the state-of-the-art methods for several standard experiments on the Extended Yale B, CMU PIE, AR, and ∗Corresponding author. Tel:+86 10 62283059 Fax: +86 10 62285019 Email address: whdeng@bupt.edu.cn (Weihong Deng) Preprint submitted to Elsevier March 28, 2014

"PASS" principles for predictable bone regeneration. <=SEP=> Guided bone regeneration is a well-established technique used for augmentation of deficient alveolar ridges. Predictable regeneration requires both a high level of technical skill and a thorough understanding of underlying principles of wound healing. This article describes the 4 major biologic principles (i.e., PASS) necessary for predictable bone regeneration: primary wound closure to ensure undisturbed and uninterrupted wound healing, angiogenesis to provide necessary blood supply and undifferentiated mesenchymal cells, space maintenance/creation to facilitate adequate space for bone ingrowth, and stability of wound and implant to induce blood clot formation and uneventful healing events. In addition, a novel flap design and clinical cases using this principle are presented.

A multilevel meta-analysis of studies reporting correlations between the h index and 37 different h index variants <=SEP=> This paper presents the first meta-analysis of studies that computed correlations between the h index and variants of the h index (such as the g index; in total 37 different variants) that have been proposed and discussed in the literature. A high correlation between the h index and its variants would indicate that the h index variants hardly provide added information to the h index. This meta-analysis included 135 correlation coefficients from 32 studies. The studies were based on a total sample size of N = 9005; on average, each study had a sample size of n = 257. The results of a three-level cross-classified mixed-effects metaanalysis show a high correlation between the h index and its variants: Depending on the model, the mean correlation coefficient varies between .8 and .9. This means that there is redundancy between most of the h index variants and the h index. There is a statistically significant study-to-study variation of the correlation coefficients in the information they yield. The lowest correlation coefficients with the h index are found for the h index variants MII and m index. Hence, these h index variants make a non-redundant contribution to the h index. © 2011 Elsevier Ltd. All rights reserved.

Autonomous Ground Vehicles—Concepts and a Path to the Future <=SEP=> Autonomous vehicles promise numerous improvements to vehicular traffic: an increase in both highway capacity and traffic flow because of faster response times, less fuel consumption and pollution thanks to more foresighted driving, and hopefully fewer accidents thanks to collision avoidance systems. In addition, drivers can save time for more useful activities. In order for these vehicles to safely operate in everyday traffic or in harsh off-road environments, a multitude of problems in perception, navigation, and control have to be solved. This paper gives an overview of the most current trends in autonomous vehicles, highlighting the concepts common to most successful systems as well as their differences. It concludes with an outlook into the promising future of autonomous vehicles.

Acupuncture and electro-acupuncture for people diagnosed with subacromial pain syndrome: A multicentre randomized trial. <=SEP=> BACKGROUND
Musculoskeletal disorders have been identified globally as the second most common healthcare problem for 'years lived with disability', and of these shoulder conditions are amongst the most common, frequently associated with substantial pain and morbidity. Exercise and acupuncture are often provided as initial treatments for musculoskeletal shoulder conditions but their clinical effectiveness is uncertain. This study compared group exercise with group exercise plus either acupuncture or electro-acupuncture in patients with subacromial pain syndrome.


METHODS
Two hundred and twenty-seven participants were recruited to a three-arm parallel-group randomized clinical trial. The primary outcome measure was the Oxford Shoulder Score. Follow-up was post treatment, and at 6 and 12 months. Between-group differences (two comparisons: the exercise group versus each of the acupuncture groups) were analysed at 6 months. A similar comparison across all follow-up time points was also conducted. Data were analysed on intention-to-treat principles with imputation of missing values.


RESULTS
Treatment groups were similar at baseline, and all treatment groups demonstrated an improvement over time. Between-group estimates at 6 months were, however, small and non-significant, for both of the comparisons. The analyses across all follow-up time points yielded similar conclusions. There was a high rate of missing values (22% for the Oxford Shoulder Score). A sensitivity analysis using complete data gave similar conclusions to the analysis with missing values imputed.


CONCLUSIONS
In the current investigation, neither acupuncture nor electro-acupuncture were found to be more beneficial than exercise alone in the treatment of subacromial pain syndrome. These findings may support clinicians with treatment planning.


SIGNIFICANCE
Shoulder pain is common and associated with substantial morbidity. Acupuncture is a popular treatment for shoulder pain. The findings suggest that acupuncture and electro-acupuncture offer no additional benefit over exercise in the treatment of shoulder pain of musculoskeletal origin.

COMPACT WIDE-SLOT TRI-BAND ANTENNA FOR WLAN/WIMAX APPLICATIONS <=SEP=> In this paper, a wide-slot triple band antenna fed by a coplanar waveguide (CPW) for WLAN/WiMAX applications is proposed. The antenna mainly comprises a ground with a wide square slot in the center, a rectangular feeding strip and two pairs of planar inverted L strips (PIL) connecting with the slotted ground. By introducing the two pairs of PIL’s, three resonant frequencies, 2.4/5.5GHz for WLAN, and 3.5 GHz for WiMAX, are excited. Prototypes of the antenna are fabricated and tested. The simulated and measured results show that the proposed antenna has three good impedance bandwidths (S11 better than −10 dB) of 300MHz (about 12.6% centered at 2.39 GHz), 280 MHz (about 8% centered at 3.49GHz) and 790 MHz (about 14.5% centered at 5.43GHz), which make it easily cover the required bandwidths for WLAN band (2.4– 2.48GHz, 5.15–5.35 GHz, and 5.725–5.825GHz) and WiMAX (3.43.6GHz) applications. Moreover, the obtained radiation patterns demonstrate that the proposed antenna has figure-eight patterns in E-plane, and is omni-directional in H-plane. The gains of the antenna at operation bands are stable.

Videos as Space-Time Region Graphs <=SEP=> How do humans recognize the action “opening a book”? We argue that there are two important cues: modeling temporal shape dynamics and modeling functional relationships between humans and objects. In this paper, we propose to represent videos as space-time region graphs which capture these two important cues. Our graph nodes are defined by the object region proposals from different frames in a long range video. These nodes are connected by two types of relations: (i) similarity relations capturing the long range dependencies between correlated objects and (ii) spatial-temporal relations capturing the interactions between nearby objects. We perform reasoning on this graph representation via Graph Convolutional Networks. We achieve state-of-the-art results on both Charades and Something-Something datasets. Especially for Charades, we obtain a huge 4.4% gain when our model is applied in complex environments.

DNA Sequence Compression Using the Burrows-Wheeler Transform <=SEP=> We investigate off-line dictionary oriented approaches to DNA sequence compression, based on the Burrows-Wheeler Transform (BWT). The preponderance of short repeating patterns is an important phenomenon in biological sequences. Here, we propose off-line methods to compress DNA sequences that exploit the different repetition structures inherent in such sequences. Repetition analysis is performed based on the relationship between the BWT and important pattern matching data structures, such as the suffix tree and suffix array. We discuss how the proposed approach can be incorporated in the BWT compression pipeline.

Enhancing an Interactive Recommendation System with Review-based Information Filtering <=SEP=> Integrating interactive faceted filtering with intelligent recommendation techniques has shown to be a promising means for increasing user control in Recommender Systems. In this paper, we extend the concept of blended recommending by automatically extracting meaningful facets from social media by means of Natural Language Processing. Concretely, we allow users to influence the recommendations by selecting facet values and weighting them based on information other users provided in their reviews. We conducted a user study with an interactive recommender implemented in the hotel domain. This evaluation shows that users are consequently able to find items fitting interests that are typically difficult to take into account when only structured content data is available. For instance, the extracted facets representing the opinions of hotel visitors make it possible to effectively search for hotels with comfortable beds or that are located in quiet surroundings without having to read the user reviews.

Comprehensive Survey on T-SDN: Software-Defined Networking for Transport Networks <=SEP=> Paradoxically, with an ever-increasing traffic demand, today transport-network operators experience a progressive erosion of their margins. The alarms of change are set, and software define networking (SDN) is coming to the rescue with the promise of reducing capital expenditures and operational expenses. Driven by economic needs and network innovation facilities, today transport SDN (T-SDN) is a reality. It gained big momentum in the last years, however, in the networking industry, the transport network will be perhaps the last segment to embrace SDN, mainly due to the heterogeneous nature and complexity of the optical equipment composing it. This survey guides the reader through a fascinating technological adventure that provides an organic analysis of the T-SDN development and evolution considering contributions from: academic research, standardization bodies, industrial development, open source projects, and alliances among them. After creating a comprehensive picture of T-SDN, we provide an analysis of many open issues that are expected to need significant future work, and give our vision in this path toward a fully programmable and dynamic transport network.

Hardware Covert Attacks and Countermeasures <=SEP=> Computing platforms deployed in many critical infrastructures, such as smart grid, financial systems, governmental organizations etc., are subjected to security attacks and potentially devastating consequences. Computing platforms often get attacked 'physically' by an intruder accessing stored information, studying the internal structure of the hardware or injecting a fault. Even if the attackers fail to gain sensitive information stored in hardware, they may be able to disrupt the hardware or deny service leading to other kinds of security failures in the system. Hardware attacks could be covert or overt, based on the awareness of the intended system. This work classifies existing hardware attacks. Focusing mainly on covert attacks, they are quantified using a proposed schema. Different countermeasure techniques are proposed to prevent such attacks.

Real-life voice activity detection with LSTM Recurrent Neural Networks and an application to Hollywood movies <=SEP=> A novel, data-driven approach to voice activity detection is presented. The approach is based on Long Short-Term Memory Recurrent Neural Networks trained on standard RASTA-PLP frontend features. To approximate real-life scenarios, large amounts of noisy speech instances are mixed by using both read and spontaneous speech from the TIMIT and Buckeye corpora, and adding real long term recordings of diverse noise types. The approach is evaluated on unseen synthetically mixed test data as well as a real-life test set consisting of four full-length Hollywood movies. A frame-wise Equal Error Rate (EER) of 33.2% is obtained for the four movies and an EER of 9.6% is obtained for the synthetic test data at a peak SNR of 0 dB, clearly outperforming three state-of-the-art reference algorithms under the same conditions.

Can e-learning replace classroom learning? <=SEP=> In an e-learning environment that emphasizes learner-centered activity and system interactivity, remote learners can outperform traditional classroom students.