Abstract: Inference of how evolutionary forces have shaped extant genetic diversity is a cornerstone of modern comparative sequence analysis. Advances in sequence generation and increased statistical sophistication of relevant methods now allow researchers to extract ever more evolutionary signal from the data, albeit at an increased computational cost. Here, we announce the release of Datamonkey 2.0, a completely re-engineered version of the Datamonkey web-server for analyzing evolutionary signatures in sequence data. For this endeavor, we leveraged recent developments in open-source libraries that facilitate interactive, robust, and scalable web application development. Datamonkey 2.0 provides a carefully curated collection of methods for interrogating coding-sequence alignments for imprints of natural selection, packaged as a responsive (i.e. can be viewed on tablet and mobile devices), fully interactive, and API-enabled web application. To complement Datamonkey 2.0, we additionally release HyPhy Vision, an accompanying JavaScript application for visualizing analysis results. HyPhy Vision can also be used separately from Datamonkey 2.0 to visualize locally executed HyPhy analyses. Together, Datamonkey 2.0 and HyPhy Vision showcase how scientific software development can benefit from general-purpose open-source frameworks. Datamonkey 2.0 is freely and publicly available at http://www.datamonkey.org, and the underlying codebase is available from https://github.com/veg/datamonkey-js.

Abstract: Summary The MetaboAnalyst web application has been widely used for metabolomics data analysis and interpretation. Despite its user-friendliness, the web interface has presented its inherent limitations (especially for advanced users) with regard to flexibility in creating customized workflow, support for reproducible analysis, and capacity in dealing with large data. To address these limitations, we have developed a companion R package (MetaboAnalystR) based on the R code base of the web server. The package has been thoroughly tested to ensure that the same R commands will produce identical results from both interfaces. MetaboAnalystR complements the MetaboAnalyst web server to facilitate transparent, flexible and reproducible analysis of metabolomics data. Availability and implementation MetaboAnalystR is freely available from https://github.com/xia-lab/MetaboAnalystR.

Abstract: Contact mapping experiments such as Hi-C explore how genomes fold in 3D. Here, we introduce Juicebox.js, a cloud-based web application for exploring the resulting datasets. Like the original Juicebox application, Juicebox.js allows users to zoom in and out of such datasets using an interface similar to Google Earth. Juicebox.js also has many features designed to facilitate data reproducibility and sharing. Furthermore, Juicebox.js encodes the exact state of the browser in a shareable URL. Creating a public browser for a new Hi-C dataset does not require coding and can be accomplished in under a minute. The web app also makes it possible to create interactive figures online that can complement or replace ordinary journal figures. When combined with Juicer, this makes the entire process of data analysis transparent, insofar as every step from raw reads to published figure is publicly available as open source code.

Abstract: Modern scientific endeavors increasingly require team collaborations to construct and interpret complex computational workflows. This work describes an image-analysis environment that supports the use of computational tools that facilitate reproducible research and support scientists with varying levels of software development skills. The Jupyter notebook web application is the basis of an environment that enables flexible, well-documented, and reproducible workflows via literate programming. Image-analysis software development is made accessible to scientists with varying levels of programming experience via the use of the SimpleITK toolkit, a simplified interface to the Insight Segmentation and Registration Toolkit. Additional features of the development environment include user friendly data sharing using online data repositories and a testing framework that facilitates code maintenance. SimpleITK provides a large number of examples illustrating educational and research-oriented image analysis workflows for free download from GitHub under an Apache 2.0 license: github.com/InsightSoftwareConsortium/SimpleITK-Notebooks .

Abstract: An improved architecture and enthusiastic user base are driving uptake of the open-source web tool. An improved architecture and enthusiastic user base are driving uptake of the open-source web tool.

Abstract: Summary BioJupies is a web application that enables the automated creation, storage, and deployment of Jupyter Notebooks containing RNA-seq data analyses. Through an intuitive interface, novice users can rapidly generate tailored reports to analyze and visualize their own raw sequencing files, gene expression tables, or fetch data from >9,000 published studies containing >300,000 preprocessed RNA-seq samples. Generated notebooks have the executable code of the entire pipeline, rich narrative text, interactive data visualizations, differential expression, and enrichment analyses. The notebooks are permanently stored in the cloud and made available online through a persistent URL. The notebooks are downloadable, customizable, and can run within a Docker container. By providing an intuitive user interface for notebook generation for RNA-seq data analysis, starting from the raw reads all the way to a complete interactive and reproducible report, BioJupies is a useful resource for experimental and computational biologists. BioJupies is freely available as a web-based application from http://biojupies.cloud .

Abstract: Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of the cloud is elasticity. It allows cloud users to acquire or release computing resources on demand, which enables web application providers to automatically scale the resources provisioned to their applications without human intervention under a dynamic workload to minimize resource cost while satisfying Quality of Service (QoS) requirements. In this article, we comprehensively analyze the challenges that remain in auto-scaling web applications in clouds and review the developments in this field. We present a taxonomy of auto-scalers according to the identified challenges and key properties. We analyze the surveyed works and map them to the taxonomy to identify the weaknesses in this field. Moreover, based on the analysis, we propose new future directions that can be explored in this area.

Abstract: For a long time, many popular listening test methods, such as ITU-R BS.1534 (MUSHRA), could not be carried out as web-based listening tests, since established web standards did not support all required audio processing features. With the standardization of the Web Audio API, the required features became available and, therefore, also the possibility to implement a wide range of established methods as web-based listening tests. In order to simplify the implementation of MUSHRA listening tests, the development of webMUSHRA was started. By utilizing webMUSHRA, experimenters can configure web-based MUSHRA listening tests without the need of web programming expertise. Today, webMUSHRA supports many more listening test methods, such as ITU-R BS.1116 and forced-choice procedures. Moreover, webMUSHRA is highly customizable and has been used in many auditory studies for different purposes.

Abstract: Biological networks play increasingly important roles in omics data integration and systems biology. Over the past decade, many excellent tools have been developed to support creation, analysis and visualization of biological networks. However, important limitations remain: most tools are standalone programs, the majority of them focus on protein-protein interaction (PPI) or metabolic networks, and visualizations often suffer from 'hairball' effects when networks become large. To help address these limitations, we developed OmicsNet - a novel web-based tool that allows users to easily create different types of molecular interaction networks and visually explore them in a three-dimensional (3D) space. Users can upload one or multiple lists of molecules of interest (genes/proteins, microRNAs, transcription factors or metabolites) to create and merge different types of biological networks. The 3D network visualization system was implemented using the powerful Web Graphics Library (WebGL) technology that works natively in most major browsers. OmicsNet supports force-directed layout, multi-layered perspective layout, as well as spherical layout to help visualize and navigate complex networks. A rich set of functions have been implemented to allow users to perform coloring, shading, topology analysis, and enrichment analysis. OmicsNet is freely available at http://www.omicsnet.ca.

Abstract: With the rapid development of service-oriented computing and cloud computing, an increasing number of Web services have been published on the Internet, which makes it difficult to select relevant Web services manually to satisfy complex user requirements. Many machine learning methods, especially matrix factorization based collaborative filtering models, have been widely employed in Web service recommendation. However, as a linear model of latent factors, matrix factorization is challenging to capture complex interactions between Web applications (or mashups) and their component services within an extremely sparse interaction matrix, which will result in poor service recommendation performance. Towards this problem, in this paper, we propose a novel deep learning based hybrid approach for Web service recommendation by combining collaborative filtering and textual content. The invocation interactions between mashups and services as well as their functionalities are seamlessly integrated into a deep neural network, which can be used to characterize the complex relations between mashups and services. Experiments conducted on a real-world Web service dataset demonstrate that our approach can achieve better recommendation performance than several state-of-the-art methods, which indicates the effectiveness of our proposed approach in service recommendation.

Abstract: This paper designs an Internet of Things system (called JustIoT) which is mainly divided into four parts; back-end Google Firebase real-time database, front-end SPA (Single Page Application) web monitoring program (including mobile monitoring App), controller software-hardware, and intelligence server that support MQTT connection and condition control. JustIoT receives data from all kinds of controllers, allowing users to set the control rules and remote monitoring and control. JustIOT distinguishes users from managers, vendors, customers, registrants, and visitors. Users can build applications in the above system, to serve customers, and to run a business. In the JustIoT, management web page based on Angular front-end technology is connected to the Firebase real-time database. The event of data modification of Firebase database can trigger Angular's two-way data binding to achieve Three-way data binding effect to implement server-less architecture easily. The data in Firebase database is read and written by the front-end devices (web apps, mobile apps, and controllers) directly. The intelligence server is an MQTT server that supports the connections of relatively weak embedded controllers such as the Arduino controller. The intelligent server can be considered as an intermediary between the Firebase real-time database and weak controllers, which performs the transfer of data and remote commands. The intelligent server is also the intelligent computing center of the JustIoT. It performs condition control.

Abstract: Web Application Programming Interfaces (APIs) allow systems to interact with each other over the network. Modern Web APIs often adhere to the REST architectural style, being referred to as RESTful Web APIs. RESTful Web APIs are decomposed into multiple resources (e.g., a video in the YouTube API) that clients can manipulate through HTTP interactions. Testing Web APIs is critical but challenging due to the difficulty to assess the correctness of API responses, i.e., the oracle problem. Metamorphic testing alleviates the oracle problem by exploiting relations (so-called metamorphic relations) among multiple executions of the program under test. In this paper, we present a metamorphic testing approach for the detection of faults in RESTful Web APIs. We first propose six abstract relations that capture the shape of many of the metamorphic relations found in RESTful Web APIs, we call these Metamorphic Relation Output Patterns (MROPs). Each MROP can then be instantiated into one or more concrete metamorphic relations. The approach was evaluated using both automatically seeded and real faults in six subject Web APIs. Among other results, we identified 60 metamorphic relations (instances of the proposed MROPs) in the Web APIs of Spotify and YouTube. Each metamorphic relation was implemented using both random and manual test data, running over 4.7K automated tests. As a result, 11 issues were detected (3 in Spotify and 8 in YouTube), 10 of them confirmed by the API developers or reproduced by other users, supporting the effectiveness of the approach.

Abstract: The web has become the dominant access mechanism for remote compute services in every computing area except high-performance computing (HPC). Accessing HPC resources, either at the campus or national level typically requires advanced knowledge of Linux, familiarity with command-line interfaces and installation and configuration of custom client software (e.g., Secure Shell (SSH) and Virtual Network Computing (VNC)). These additional requirements create an accessibility gap for HPC. To help address this gap we have created the Open OnDemand Project (Hudak et al., 2016), an open-source software project based on the proven Ohio Supercomputer Center (OSC) OnDemand platform (Hudak et al., 2013), to allow HPC centers to provide advanced web and graphical interfaces for their users.

Abstract: Motivation We recently published MS2LDA, a method for the decomposition of sets of molecular fragment data derived from large metabolomics experiments. To make the method more widely available to the community, here we present ms2lda.org, a web application that allows users to upload their data, run MS2LDA analyses and explore the results through interactive visualizations. Results Ms2lda.org takes tandem mass spectrometry data in many standard formats and allows the user to infer the sets of fragment and neutral loss features that co-occur together (Mass2Motifs). As an alternative workflow, the user can also decompose a data set onto predefined Mass2Motifs. This is accomplished through the web interface or programmatically from our web service. Availability and implementation The website can be found at http://ms2lda.org, while the source code is available at https://github.com/sdrogers/ms2ldaviz under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract: Machine leaning apps require heavy computations, especially with the use of the deep neural network (DNN), so an embedded device with limited hardware cannot run the apps by itself. One solution for this problem is to offload DNN computations from the client to a nearby edge server. Existing approaches to DNN offloading with edge servers either specialize the edge server for fixed, specific apps, or customize the edge server for diverse apps, yet after migrating a large VM image that contains the client's back-end software system. In this paper, we propose a new and simple approach to offload DNN computations in the context of web apps. We migrate the current execution state of a web app from the client to the edge server just before executing a DNN computation, so that the edge server can execute the DNN computation with its powerful hardware. Then, we migrate the new execution state from the edge server to the client so that the client can continue to execute the app. We can save the execution state of the web app in the form of another web app called the snapshot, which immensely simplifies saving and restoring the execution state with a small overhead. We can offload any DNN app to any generic edge server, equipped with a browser and our offloading system. We address some issues related to offloading DNN apps such as how to send the DNN model and how to improve the privacy of user data. We also discuss how to install our offloading system on the edge server on demand. Our experiment with real DNN-based web apps shows that snapshot-based offloading achieves a promising performance result, comparable to running the app entirely on the server.

Abstract: Web Application Programming Interfaces (APIs) specify how to access services and data over the network, typically using Web services. Web APIs are rapidly proliferating as a key element to foster reusability, integration, and innovation, enabling new consumption models such as mobile or smart TV apps. Companies such as Facebook, Twitter, Google, eBay or Netflix receive billions of API calls every day from thousands of different third-party applications and devices, which constitutes more than half of their total traffic. As Web APIs are progressively becoming the cornerstone of software integration, their validation is getting more critical. In this context, the fast detection of bugs is of utmost importance to increase the quality of internal products and third-party applications. However, testing Web APIs is challenging mainly due to the difficulty to assess whether the output of an API call is correct, i.e., the oracle problem. For instance, consider the Web API of the popular music streaming service Spotify. Suppose a search for albums with the query "redhouse" returning 21 total matches: Is this output correct? Do all the albums in the result set contain the keyword? Are there any albums containing the keyword not included in the result set? Answering these questions is difficult, even with small result sets, and often infeasible when the results are counted by thousands or millions. Metamorphic testing alleviates the oracle problem by providing an alternative when the expected output of a test execution is complex or unknown. Rather than checking the output of an individual program execution, metamorphic testing checks whether multiple executions of the program under test fulfil certain necessary properties called metamorphic relations. For instance, consider the following metamorphic relation in Spotify: two searches for albums with the same query should return the same number of total results regardless of the size of pagination. Suppose that a new Spotify search is performed using the exact same query as before and increasing the maximum number of results per page from 20 (default value) to 50: This search returns 27 total albums (6 more matches than in the previous search), which reveals a bug. This is an example of a real and reproducible fault detected using the approach presented in this paper and reported to Spotify. According to Spotify developers, it was a regression fault caused by a fix with undesired side effects. In this paper [1], we present a metamorphic testing approach for the automated detection of faults in RESTful Web APIs (henceforth also referred to as simply Web APIs). We introduce the concept of metamorphic relation output patterns. A Metamorphic Relation Output Pattern (MROP) defines an abstract output relation typically identified in Web APIs, regardless of their application domain. Each MROP is defined in terms of set operations among test outputs such as equality, union, subset, or intersection. MROPs provide a helpful guide for the identification of metamorphic relations, broadening the scope of our work beyond a particular Web API. Based on the notion of MROP, a methodology is proposed for the application of the approach to any Web API following the REST architectural pattern. The approach was evaluated in several steps. First, we used the proposed methodology to identify 33 metamorphic relations in four Web APIs developed by undergraduate students. All the relations are instances of the proposed MROPs. Then, we assessed the effectiveness of the identified relations at revealing 317 automatically seeded faults (i.e., mutants) in the APIs under test. As a result, 302 seeded faults were detected, achieving a mutation score of 95.3%. Second, we evaluated the approach using real Web APIs and faults. In particular, we identified 20 metamorphic relations in the Web API of Spotify and 40 metamorphic relations in the Web API of YouTube. Each metamorphic relation was implemented and automatically executed using both random and manual test data. In total, 469K metamorphic tests were generated. As a result, 21 metamorphic relations were violated, and 11 issues revealed and reported (3 issues in Spotify and 8 issues in YouTube). To date, 10 of the reported issues have been either confirmed by the API developers or reproduced by other users supporting the effectiveness of our approach.

Abstract: Although development practices for apps have matured, cross-platform development remains a prominent topic. Typically, apps should always support both Android and iOS devices. They ought to run smoothly on various hardware, and be compatible with a host of platform versions. Additionally, device categories beyond smartphone and tablets have emerged, which makes multi-platform support even trickier. Truly developing an app once and serving the multitude of possible targets remains an issue despite having crossplatform frameworks that are acknowledged by practice and research. The technology unifier remains to be found, but Progressive Web Apps (PWA) might be a step towards it. In this paper, we analyse the foundations of PWAs in cross-platform development and scrutinize the status quo of current possibilities. Based on our observations, we investigate unified development, and discuss open questions. We seek to stimulate interest and narrow the immense gap that has arisen since industry started to embrace PWAs.

Abstract: The Internet of Things(IoT) has made it possible to set up a smart home security through which you can decide who can enter your home using your smartphone and web application. It's also made it simple and relatively affordable to monitor your home anytime and anywhere. the key issue in a traditional home security system is, it is easily breakable and quite outdated. This in turns, results in the robbery and also needs installation of the costly security system. To tackle this problem, we propose a smart home security system, which is IoT as well as face recognition enabled. In our system, the web camera is used which is connected to the raspberry pi accompanied by sensors such as Passive Infrared(Pir) and Ultrasonic sensor. On motion detection camera captures an image of the person in front of the door then real-time face recognition is done using local binary pattern (LBP). If person's image matches with one of the home members then the door will unlock, else doorbell will ring. if an intruder tries to break door then an alarm will be raised at the same time SMS and Email containing image of the intruder will be sent to the homeowner. Face recognition works well in multi-face recognition and stranger identification, which meet the requirement of home security. This system is battery powered in case of power failure. Furthermore, the house owner can keep track of activity happening in the house using android and web application connected to the raspberry pi using the internet. Using Android application or web application owner can also add new person's faces into the databases eg., guests.

Abstract: In order to explore the communication methods of microservice web application, this paper uses RabbitMQ and REST API respectively as the message-oriented middleware of microservice web applications. We do experiments with both of the methods under various number of users to compare and evaluate their performance in different circumstances. The purpose is to provide understanding inside the two methods for microservice web applications so that service providers can select the appropriate method based on their need. Obtained experimental results show that when a large number of users send requests to the web application at the same time, it is more stable to use RabbitMQ as the Message-oriented middleware than the REST API communication method.

Abstract: Web-based Augmented Reality (Web AR) provides a lightweight, cross-platform, and pervasive AR solution. However, all of the current Web AR implementations still face some challenges, which greatly hinder the promotion of Web AR applications. Benefiting from Mobile Edge Computing (MEC) paradigm, in this paper, we propose a MEC-based collaborative Web AR solution, which can be regarded as a feasible and promising one. The edge server not only reduces the network latency but also decreases the bandwidth usage of core networks. Prototype implementation demonstrated the effectiveness and practicability of the proposed MEC-based solution for real-world Web AR development and deployment.

Abstract: Context: Software evolution ensures that software systems in use stay up to date and provide value for end-users. However, it is challenging for requirements engineers to continuously elicit needs for systems used by heterogeneous end-users who are out of organisational reach. Objective: We aim at supporting continuous requirements elicitation by combining user feedback and usage monitoring. Online feedback mechanisms enable end-users to remotely communicate problems, experiences, and opinions, while monitoring provides valuable information about runtime events. It is argued that bringing both information sources together can help requirements engineers to understand end-user needs better. Method/Tool: We present FAME, a framework for the combined and simultaneous collection of feedback and monitoring data in web and mobile contexts to support continuous requirements elicitation. In addition to a detailed discussion of our technical solution, we present the first evidence that FAME can be successfully introduced in real-world contexts. Therefore, we deployed FAME in a web application of a German small and medium-sized enterprise (SME) to collect user feedback and usage data. Results/Conclusion: Our results suggest that FAME not only can be successfully used in industrial environments but that bringing feedback and monitoring data together helps the SME to improve their understanding of end-user needs, ultimately supporting continuous requirements elicitation.

Abstract: It is a common practice for client-side web applications to build on various third-party JavaScript libraries. Due to the lack of namespaces in JavaScript, these libraries all share the same global namespace. As a result, one library may inadvertently modify or even delete the APIs of another library, causing unexpected behavior of library clients. Given the quickly increasing number of libraries, manually keeping track of such conflicts is practically impossible both for library developers and users. This paper presents ConflictJS, an automated and scalable approach to analyze libraries for conflicts. The key idea is to tackle the huge search space of possible conflicts in two phases. At first, a dynamic analysis of individual libraries identifies pairs of potentially conflicting libraries. Then, targeted test synthesis validates potential conflicts by creating a client application that suffers from a conflict. The overall approach is free of false positives, in the sense that it reports a problem only when such a client exists. We use ConflictJS to analyze and study conflicts among 951 real-world libraries. The results show that one out of four libraries is potentially conflicting and that 166 libraries are involved in at least one certain conflict. The detected conflicts cause crashes and other kinds of unexpected behavior. Our work helps library developers to prevent conflicts, library users to avoid combining conflicting libraries, and provides evidence that designing a language without explicit namespaces has undesirable effects.

Abstract: Web Application Firewalls (WAFs) are deployed to protect web applications and they offer in depth security as long as they are configured correctly. A problem arises when there is over-reliance on these tools. A false sense of security can be obtained with the implementation of a WAF. In this paper, we provide an overview of traffic filtering models and some suggestions to avail the benefit of web app firewall.

Abstract: Modern multi-tier web applications are composed of several dynamic features, which make their vulnerability analysis challenging from a purely static analysis perspective. We describe an approach that overcomes the challenges posed by the dynamic nature of web applications. Our approach combines dynamic analysis that is guided by static analysis techniques in order to automatically identify vulnerabilities and build working exploits. Our approach is implemented and evaluated in NAVEX, a tool that can scale the process of automatic vulnerability analysis and exploit generation to large applications and to multiple classes of vulnerabilities. In our experiments, we were able to use NAVEX over a codebase of 3.2 million lines of PHP code, and construct 204 exploits in the code that was analyzed.

Abstract: Online job portals collecting web vacancies have become important media for job demand and supply matching. They also represent a growing research area for the application of analytical methods to study the labour market using innovative data sources. This paper analyses Italian web job vacancies scraped from several types of Italian web job portals between June and September 2015. After describing how the occupations associated with each web vacancy (classification up to level 4) were identified and the related skills retrieved in texts using mixed supervised and unsupervised text mining approaches, we focused on job vacancies related to ICT and statistical positions.

Abstract: In this paper, we present two web-based attacks against local IoT devices that any malicious web page or third-party script can perform, even when the devices are behind NATs. In our attack scenario, a victim visits the attacker's website, which contains a malicious script that communicates with IoT devices on the local network that have open HTTP servers. We show how the malicious script can circumvent the same-origin policy by exploiting error messages on the HTML5 MediaError interface or by carrying out DNS rebinding attacks. We demonstrate that the attacker can gather sensitive information from the devices (e.g., unique device identifiers and precise geolocation), track and profile the owners to serve ads, or control the devices by playing arbitrary videos and rebooting. We propose potential countermeasures to our attacks that users, browsers, DNS providers, and IoT vendors can implement.

Abstract: We consider the problem of predicting the success of startup companies at their early development stages. We formulate the task as predicting whether a company that has already secured initial (seed or angel) funding will attract a further round of investment in a given period of time. Previous work on this task has mostly been restricted to mining structured data sources, such as databases of the startup ecosystem consisting of investors, incubators and startups. Instead, we investigate the potential of using web-based open sources for the startup success prediction task and model the task using a very rich set of signals from such sources. In particular, we enrich structured data about the startup ecosystem with information from a business- and employment-oriented social networking service and from the web in general. Using these signals, we train a robust machine learning pipeline encompassing multiple base models using gradient boosting. We show that utilizing companies' mentions on the Web yields a substantial performance boost in comparison to only using structured data about the startup ecosystem. We also provide a thorough analysis of the obtained model that allows one to obtain insights into both the types of useful signals discoverable on the Web and market mechanisms underlying the funding process.

Abstract: Despite the extensive research of using web services for security purposes, there is a big challenge towards finding a no radical solution for NoSQL injection attack. This paper presents an independent RESTful web service in a layered approach to detect NoSQL injection attacks in web applications. The proposed method is named DNIARS. DNIARS depends on comparing the generated patterns from NoSQL statement structure in static code state and dynamic state. Accordingly, the DNIARS can respond to the web application with the possibility of NoSQL injection attack. The proposed DNIARS was implemented in PHP plain code and can be considered as an independent framework that has the ability for responding to different requests formats like JSON, XML. To evaluate its performance, DNIARS was tested using the most common testing tools for RESTful web service. According to the results, DNIARS can work in real environments where the error rate did not exceed 1%.

Abstract: SensePlace3 (SP3) is a geovisual analytics framework and web application that supports overview + detail analysis of social media, focusing on extracting meaningful information from the Twitterverse. SP3 leverages social media related to crisis events. It differs from most existing systems by enabling an analyst to obtain place-relevant information from tweets that have implicit as well as explicit geography. Specifically, SP3 includes not just the ability to utilize the explicit geography of geolocated tweets but also analyze implicit geography by recognizing and geolocating references in both tweet text, which indicates locations tweeted about, and in Twitter profiles, which indicates locations affiliated with users. Key features of SP3 reported here include flexible search and filtering capabilities to support information foraging; an ingest, processing, and indexing pipeline that produces near real-time access for big streaming data; and a novel strategy for implementing a web-based multi-view...