Showing papers on "Web service published in 2016"

deepTools2: a next generation web server for deep-sequencing data analysis

Abstract: We present an update to our Galaxy-based web server for processing and visualizing deeply sequenced data. Its core tool set, deepTools, allows users to perform complete bioinformatic workflows ranging from quality controls and normalizations of aligned reads to integrative analyses, including clustering and visualization approaches. Since we first described our deepTools Galaxy server in 2014, we have implemented new solutions for many requests from the community and our users. Here, we introduce significant enhancements and new tools to further improve data visualization and interpretation. deepTools continue to be open to all users and freely available as a web service at deeptools.ie-freiburg.mpg.de The new deepTools2 suite can be easily deployed within any Galaxy framework via the toolshed repository, and we also provide source code for command line usage under Linux and Mac OS X. A public and documented API for access to deepTools functionality is also available.

The MPI bioinformatics Toolkit as an integrative platform for advanced protein sequence and structure analysis

Abstract: The MPI Bioinformatics Toolkit (http://toolkit.tuebingen.mpg.de) is an open, interactive web service for comprehensive and collaborative protein bioinformatic analysis. It offers a wide array of interconnected, state-of-the-art bioinformatics tools to experts and non-experts alike, developed both externally (e.g. BLAST+, HMMER3, MUSCLE) and internally (e.g. HHpred, HHblits, PCOILS). While a beta version of the Toolkit was released 10 years ago, the current production-level release has been available since 2008 and has serviced more than 1.6 million external user queries. The usage of the Toolkit has continued to increase linearly over the years, reaching more than 400 000 queries in 2015. In fact, through the breadth of its tools and their tight interconnection, the Toolkit has become an excellent platform for experimental scientists as well as a useful resource for teaching bioinformatic inquiry to students in the life sciences. In this article, we report on the evolution of the Toolkit over the last ten years, focusing on the expansion of the tool repertoire (e.g. CS-BLAST, HHblits) and on infrastructural work needed to remain operative in a changing web environment.

3Drefine: an interactive web server for efficient protein structure refinement

Abstract: 3Drefine is an interactive web server for consistent and computationally efficient protein structure refinement with the capability to perform web-based statistical and visual analysis. The 3Drefine refinement protocol utilizes iterative optimization of hydrogen bonding network combined with atomic-level energy minimization on the optimized model using a composite physics and knowledge-based force fields for efficient protein structure refinement. The method has been extensively evaluated on blind CASP experiments as well as on large-scale and diverse benchmark datasets and exhibits consistent improvement over the initial structure in both global and local structural quality measures. The 3Drefine web server allows for convenient protein structure refinement through a text or file input submission, email notification, provided example submission and is freely available without any registration requirement. The server also provides comprehensive analysis of submissions through various energy and statistical feedback and interactive visualization of multiple refined models through the JSmol applet that is equipped with numerous protein model analysis tools. The web server has been extensively tested and used by many users. As a result, the 3Drefine web server conveniently provides a useful tool easily accessible to the community. The 3Drefine web server has been made publicly available at the URL: http://sysbio.rnet.missouri.edu/3Drefine/.

Serverless computation with openLambda

Abstract: We present OpenLambda, a new, open-source platform for building next-generation web services and applications in the burgeoningmodel of serverless computation. We describe the key aspects of serverless computation, and present numerous research challenges that must be addressed in the design and implementation of such systems. We also include a brief study of current web applications, so as to better motivate some aspects of serverless application construction.

Analyzing Android Encrypted Network Traffic to Identify User Actions

Abstract: Mobile devices can be maliciously exploited to violate the privacy of people. In most attack scenarios, the adversary takes the local or remote control of the mobile device, by leveraging a vulnerability of the system, hence sending back the collected information to some remote web service. In this paper, we consider a different adversary, who does not interact actively with the mobile device, but he is able to eavesdrop the network traffic of the device from the network side (e.g., controlling a Wi-Fi access point). The fact that the network traffic is often encrypted makes the attack even more challenging. In this paper, we investigate to what extent such an external attacker can identify the specific actions that a user is performing on her mobile apps. We design a system that achieves this goal using advanced machine learning techniques. We built a complete implementation of this system, and we also run a thorough set of experiments, which show that our attack can achieve accuracy and precision higher than 95%, for most of the considered actions. We compared our solution with the three state-of-the-art algorithms, and confirming that our system outperforms all these direct competitors.

Beauty and the Beast: Diverting Modern Web Browsers to Build Unique Browser Fingerprints

Abstract: Worldwide, the number of people and the time spent browsing the web keeps increasing. Accordingly, the technologies to enrich the user experience are evolving at an amazing pace. Many of these evolutions provide for a more interactive web (e.g., boom of JavaScript libraries, weekly innovations in HTML5), a more available web (e.g., explosion of mobile devices), a more secure web (e.g., Flash is disappearing, NPAPI plugins are being deprecated), and a more private web (e.g., increased legislation against cookies, huge success of extensions such as Ghostery and AdBlock). Nevertheless, modern browser technologies, which provide the beauty and power of the web, also provide a darker side, a rich ecosystem of exploitable data that can be used to build unique browser fingerprints. Our work explores the validity of browser fingerprinting in today's environment. Over the past year, we have collected 118,934 fingerprints composed of 17 attributes gathered thanks to the most recent web technologies. We show that innovations in HTML5 provide access to highly discriminating attributes, notably with the use of the Canvas API which relies on multiple layers of the user's system. In addition, we show that browser fingerprinting is as effective on mobile devices as it is on desktops and laptops, albeit for radically different reasons due to their more constrained hardware and software environments. We also evaluate how browser fingerprinting could stop being a threat to user privacy if some technological evolutions continue (e.g., disappearance of plugins) or are embraced by browser vendors (e.g., standard HTTP headers).

High-performance web services for querying gene and variant annotation

Abstract: Efficient tools for data management and integration are essential for many aspects of high-throughput biology. In particular, annotations of genes and human genetic variants are commonly used but highly fragmented across many resources. Here, we describe MyGene.info and MyVariant.info, high-performance web services for querying gene and variant annotation information. These web services are currently accessed more than three million times permonth. They also demonstrate a generalizable cloud-based model for organizing and querying biological annotation information. MyGene.info and MyVariant.info are provided as high-performance web services, accessible at http://mygene.info and http://myvariant.info . Both are offered free of charge to the research community.

An Integrated Semantic Web Service Discovery and Composition Framework

Abstract: In this paper we present a theoretical analysis of graph-based service composition in terms of its dependency with service discovery. Driven by this analysis we define a composition framework by means of integration with fine-grained I/O service discovery that enables the generation of a graph-based composition which contains the set of services that are semantically relevant for an input-output request. The proposed framework also includes an optimal composition search algorithm to extract the best composition from the graph minimising the length and the number of services, and different graph optimisations to improve the scalability of the system. A practical implementation used for the empirical analysis is also provided. This analysis proves the scalability and flexibility of our proposal and provides insights on how integrated composition systems can be designed in order to achieve good performance in real scenarios for the web.

Thingspeak Based Sensing and Monitoring System for IoT with Matlab Analysis

Abstract: As the expeditious of Internet of Things (IoT) is emerging and is accustom for remote monitoring of the surrounding parameters and other stuffs with the use of sensors that acquaint for wireless sensing of real time data and transfer them into the desired form and help to forward the sensed data across the network cloud via lsquoInternet Connectionrsquo. Here the project work deals with The IoT lsquoThingspeakrsquo web service which is a generous open API service that act as a host for the variety of sensors to monitor the sensed data at cloud level and composite a special feature of porting the sensed data to the MATLAB R2016a using a channel ID and read API key that is assigned by services and able to track data value at picky sample at particular intervals. This project also uses an Arduino UNO board, ESP8266 Wi-Fi Module that helps to process and transfer the sensed data to the Thingspeak Cloud.

Service Selection for Composition with QoS Correlations

Abstract: QoS as an important criterion has attracted more and more attention in the service selection process. Various QoS-aware service selection methods have been proposed in recent years. However, few of them take into account of the QoS correlations between services, causing several performance issues. QoS correlations can be defined as that some QoS attributes of a service are not only dependent on the service itself but are also correlated to other services. Since such correlations will affect QoS values, it is important to study how to select appropriate candidate services while taking into account of QoS correlations when generating composite services with optimal QoS values. To this end, we propose a novel method of service selection, called the correlation-aware service pruning (CASP) method. It manages QoS correlations by accounting for all services that may be integrated into optimal composite services and prunes services that are not the optimal candidate services. Our experiments show that this method can manage complicated correlations between services and significantly improve the QoS values of the generated composite services.

Location-Aware and Personalized Collaborative Filtering for Web Service Recommendation

Abstract: Collaborative Filtering (CF) is widely employed for making Web service recommendation. CF-based Web service recommendation aims to predict missing QoS (Quality-of-Service) values of Web services. Although several CF-based Web service QoS prediction methods have been proposed in recent years, the performance still needs significant improvement. First, existing QoS prediction methods seldom consider personalized influence of users and services when measuring the similarity between users and between services. Second, Web service QoS factors, such as response time and throughput, usually depends on the locations of Web services and users. However, existing Web service QoS prediction methods seldom took this observation into consideration. In this paper, we propose a location-aware personalized CF method for Web service recommendation. The proposed method leverages both locations of users and Web services when selecting similar neighbors for the target user or service. The method also includes an enhanced similarity measurement for users and Web services, by taking into account the personalized influence of them. To evaluate the performance of our proposed method, we conduct a set of comprehensive experiments using a real-world Web service dataset. The experimental results indicate that our approach improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods.

Workload characterization for microservices

Abstract: The microservice architecture is a new framework to construct a Web service as a collection of small services that communicate with each other. It is becoming increasingly popular because it can accelerate agile software development, deployment, and operation practices. As a result, cloud service providers are expected to host an increasing number of microservices that can generate significant resource pressure on the cloud infrastructure. We want to understand the characteristics of microservice workloads to design an infrastructure optimized for microservices. In this paper, we used Acme Air, an open-source benchmark for Web services, and analyzed the behavior of two versions of the benchmark, microservice and monolithic, for two widely used language runtimes, Node.js and Java. We observed a significant overhead due to the microservice architecture; the performance of the microservice version can be 79.2% lower than the monolithic version on the same hardware configuration. On Node.js, the microservice version consumed 4.22 times more time in the libraries of Node.js than the monolithic version to process one user request. On Java, the microservice version also consumed more time in the application server than the monolithic version. We explain these performance differences from both hardware and software perspectives. We discuss the network virtualization in Docker, an infrastructure for microservices that has nonnegligible impact on performance. These findings give clues to develop optimization techniques in a language runtime and hardware for microservice workloads.

Visualising the digital twin using web services and augmented reality

Abstract: As the number of network connected devices in an industrial system increases, the management and handling of all the information generated become a challenge. In this work we explore concepts of Cyber-Physical System model the virtual part of industrial devices (sensor, machines, CLPs) using the Digital Twin concept and propose an architecture based on web services for accessing their data. We present a case study where an Augmented Reality system access the Twin Model data via web services and display real-time information to the user. Moreover, we present a review of how the involved concepts, which have a strong computational background, relate to industrial applications and how they can expand the possibility of services and business models.

PHYLOViZ Online: web-based tool for visualization, phylogenetic inference, analysis and sharing of minimum spanning trees.

Abstract: High-throughput sequencing methods generated allele and single nucleotide polymorphism information for thousands of bacterial strains that are publicly available in online repositories and created the possibility of generating similar information for hundreds to thousands of strains more in a single study. Minimum spanning tree analysis of allelic data offers a scalable and reproducible methodological alternative to traditional phylogenetic inference approaches, useful in epidemiological investigations and population studies of bacterial pathogens. PHYLOViZ Online was developed to allow users to do these analyses without software installation and to enable easy accessing and sharing of data and analyses results from any Internet enabled computer. PHYLOViZ Online also offers a RESTful API for programmatic access to data and algorithms, allowing it to be seamlessly integrated into any third party web service or software. PHYLOViZ Online is freely available at https://online.phyloviz.net.

Context-aware QoS prediction for web service recommendation and selection

Abstract: QoS prediction is one of the key problems in Web service recommendation and selection. The context information is a dominant factor affecting QoS, but is ignored by most of existing works. In this paper, we employ the context information, from both the user side and service side, to achieve superior QoS prediction accuracy. We propose two novel prediction models, which are capable of using the context information of users and services respectively. In the user side, we use the geographical information as the user context, and identify similar neighbors for each user based on the similarity of their context. We study the mapping relationship between the similarity value and the geographical distance. In the service side, we use the affiliation information as the service context, including the company affiliation and country affiliation. In the two models, the prediction value is learned by the QoS records of a user (or a service) and the neighbors. Also, we propose an ensemble model to combine the results of the two models. We conduct comprehensive experiments in two real-world datasets, and the experimental results demonstrate the effectiveness of our models.

WebVectors: A Toolkit for Building Web Interfaces for Vector Semantic Models

Abstract: The paper presents a free and open source toolkit which aim is to quickly deploy web services handling distributed vector models of semantics. It fills in the gap between training such models (many tools are already available for this) and dissemination of the results to general public. Our toolkit, WebVectors, provides all the necessary routines for organizing online access to querying trained models via modern web interface. We also describe two demo installations of the toolkit, featuring several efficient models for English, Russian and Norwegian.

A Web of Things Framework for RESTful Applications and Its Experimentation in a Smart City

Abstract: The Web of Things is an active research field which aims at promoting the easy access and handling of smart things' digital representations through the adoption of Web standards and technologies. While huge research and development efforts have been spent on lower level networks and software technologies, it has been recognized that little experience exists instead in modeling and building applications for the Web of Things. Although several works have proposed Representational State Transfer (REST) inspired approaches for the Web of Things, a main limitation is that poor support is provided to web developers for speeding up the development of Web of Things applications while taking full advantage of REST benefits. In this paper, we propose a framework which supports developers in modeling smart things as web resources, exposing them through RESTful Application Programming Interfaces (APIs) and developing applications on top of them. The framework consists of a Web Resource information model, a middleware, and tools for developing and publishing smart things' digital representations on the Web. We discuss the framework compliance with REST guidelines and its major implementation choices. Finally, we report on our test activities carried out within the SmartSantander European Project to evaluate the use and proficiency of our framework in a smart city scenario.

A Highly Accurate Prediction Algorithm for Unknown Web Service QoS Values

Abstract: Quality of service (QoS) guarantee is an important component of service recommendation. Generally, some QoS values of a service are unknown to its users who has never invoked it before, and therefore the accurate prediction of unknown QoS values is significant for the successful deployment of web service-based applications. Collaborative filtering is an important method for predicting missing values, and has thus been widely adopted in the prediction of unknown QoS values. However, collaborative filtering originated from the processing of subjective data, such as movie scores. The QoS data of web services are usually objective, meaning that existing collaborative filtering-based approaches are not always applicable for unknown QoS values. Based on real world web service QoS data and a number of experiments, in this paper, we determine some important characteristics of objective QoS datasets that have never been found before. We propose a prediction algorithm to realize these characteristics, allowing the unknown QoS values to be predicted accurately. Experimental results show that the proposed algorithm predicts unknown web service QoS values more accurately than other existing approaches.

MAESTROweb: a web server for structure-based protein stability prediction

Abstract: Summary: The prediction of change in stability upon point mutations in proteins has many applications in protein analysis and engineering. We recently adjoined a new structure-based method called MAESTRO, which is distributed as command line program. We now provide access to the most important features of MAESTRO by an easy to use web service. MAESTROweb allows the prediction of change in stability for user-defined mutations, provides a scan functionality for the most (de)stabilizing n-point mutations for a maximum of n = 5, creates mutation sensitivity profiles and evaluates potential disulfide bonds. MAESTROweb operates on monomers, multimers and biological assemblies as defined by PDB. Availability and implementation: MAESTROweb is freely available for non-commercial use at https://biwww.che.sbg.ac.at/maestro/web. Contact: peter.lackner@sbg.ac.at

Fine-Grained Two-Factor Access Control for Web-Based Cloud Computing Services

Abstract: In this paper, we introduce a new fine-grained two-factor authentication (2FA) access control system for web-based cloud computing services. Specifically, in our proposed 2FA access control system, an attribute-based access control mechanism is implemented with the necessity of both a user secret key and a lightweight security device. As a user cannot access the system if they do not hold both, the mechanism can enhance the security of the system, especially in those scenarios where many users share the same computer for web-based cloud services. In addition, attribute-based control in the system also enables the cloud server to restrict the access to those users with the same set of attributes while preserving user privacy, i.e., the cloud server only knows that the user fulfills the required predicate, but has no idea on the exact identity of the user. Finally, we also carry out a simulation to demonstrate the practicability of our proposed 2FA system.

Building the Web of Things: With examples in Node.js and Raspberry Pi

Abstract: Summary A hands-on guide that will teach how to design and implement scalable, flexible, and open IoT solutions using web technologies. This book focuses on providing the right balance of theory, code samples, and practical examples to enable you to successfully connect all sorts of devices to the web and to expose their services and data over REST APIs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Because the Internet of Things is still new, there is no universal application protocol. Fortunately, the IoT can take advantage of the web, where IoT protocols connect applications thanks to universal and open APIs. About the Book Building the Web of Things is a guide to using cutting-edge web technologies to build the IoT. This step-by-step book teaches you how to use web protocols to connect real-world devices to the web, including the Semantic and Social Webs. Along the way you'll gain vital concepts as you follow instructions for making Web of Things devices. By the end, you'll have the practical skills you need to implement your own web-connected products and services. What's Inside Introduction to IoT protocols and devices Connect electronic actuators and sensors (GPIO) to a Raspberry Pi Implement standard REST and Pub/Sub APIs with Node.js on embedded systems Learn about IoT protocols like MQTT and CoAP and integrate them to the Web of Things Use the Semantic Web (JSON-LD, RDFa, etc.) to discover and find Web Things Share Things via Social Networks to create the Social Web of Things Build a web-based smart home with HTTP and Web Socket Compose physical mashups with EVRYTHNG, Node-RED, and IFTTT About the Reader For both seasoned programmers and those with only basic programming skills. About the Authors Dominique Guinard and Vlad Trifa pioneered the Web of Things and cofounded EVRYTHNG, a large-scale IoT cloud powering billions of Web Things.

Real-time environmental sensor data

Abstract: While real-time sensor feeds have the potential to transform both environmental science and decision-making, such data are rarely part of real-time workflows, analyses and modeling tool chains. Despite benefits ranging from detecting malfunctioning sensors to adaptive sampling, the limited number and complexity of existing real-time platforms across environmental domains pose a barrier to the adoption of real-time data. We present an architecture built upon 1) the increasing availability of new technologies to expose environmental sensors as web services, and 2) the merging of these services under recent innovations on the Internet of Things (IoT). By leveraging recent developments in the IoT arena, the environmental sciences stand to make significant gains in the use of real-time data. We describe a use case in the hydrologic sciences, where an adaptive sampling algorithm is successfully deployed to optimize the use of a constrained sensor network resource. Leveraging Internet of Things (IoT) services to support environmental sensing.A real-time architecture for the measurement and control of environmental systems.Demonstrating benefits of real-time data through adaptive sampling of water quality.

From the Service-Oriented Architecture to the Web API Economy

Abstract: As Web APIs become the backbone of Web, cloud, mobile, and machine learning applications, the services computing community will need to expand and embrace opportunities and challenges from these domains.

Collaborative Web Service Quality Prediction via Exploiting Matrix Factorization and Network Map

Abstract: Quality of services (QoS) is an important concern in web service recommendation or selection. Predicting QoS values of web services based on their historical QoS records is an effective way to acquire web service QoS, and thus has attracted considerable research interests. Recently, matrix factorization (MF), a well-known model-based collaborative filtering (CF) technique, has been successfully applied to the web service QoS prediction. It is generally believed that MF can significantly outperform traditional memory-based CF techniques. However, previous work seldom considered the influence of the underlying network on web service QoS when adopting MF for web service QoS prediction. Hence, the prediction performance is not good enough. In this paper, we propose a network-aware web service QoS prediction approach by integrating MF with the network map. By employing the network map, network distances between service users can be measured and neighborhoods of users are identified. Then, the traditional MF model is revamped by incorporating the constraint term that neighbor users are likely to perceive similar QoS of web services. Experiments conducted on two real-world web service datasets indicate that our approach outperforms previous MF and CF-based approaches in prediction accuracy.