The proposal looks very interesting, wiki link is https://wiki.apache.org/incubator/SensSoftProposal
It's great to see community building efforts around open source usability tools! -- Alex On Fri, May 27, 2016 at 8:11 AM, Mattmann, Chris A (3980) < chris.a.mattm...@jpl.nasa.gov> wrote: > Here, here. > > The team is well poised for Incubation and trying to grow hopefully > a larger community here at the ASF. > > ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ > Chris Mattmann, Ph.D. > Chief Architect > Instrument Software and Science Data Systems Section (398) > NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA > Office: 168-519, Mailstop: 168-527 > Email: chris.a.mattm...@nasa.gov > WWW: http://sunset.usc.edu/~mattmann/ > ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ > Director, Information Retrieval and Data Science Group (IRDS) > Adjunct Associate Professor, Computer Science Department > University of Southern California, Los Angeles, CA 90089 USA > WWW: http://irds.usc.edu/ > ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ > > > > > > > > > > > On 5/24/16, 12:19 PM, "Poore, Joshua C." <jpo...@draper.com> wrote: > > >Hi Ted! > > > >DARPA XDATA is ending next March. We currently have support on another > DARPA contract that is good through next year, excluding potential options. > We also have some commercial contracts that will leverage these open source > projects. Some of this funding can be used for community support and open > source product support, as well. > > > >SensSoft is also being backed heavily by Draper. Currently this means > that our programs offices are aggressively pursuing new contracts that > leverage this project. We are also working with internal leadership on > internal research and development funding for SensSoft. This is how we are > working to make sure that SensSoft survives and thrives beyond the XDATA > program that spawned it. > > > >To summarize, Draper is a soft money operation (we’re a not-for-profit). > However, we are pushing hard to support the community around SensSoft > wherever possible and are considering options for how to fold in overhead > earned on dollars brought in for SensSoft projects to support the SensSoft > community. Draper believes that inclusion into the Apache Foundation will > help increase our visibility, and help harden these projects in ways that > will help generate more revenue to continually support and build upon the > project. > > > >Thanks, > > > >Josh > > > > > >Joshua C. Poore, Ph.D. > >Senior Member of the Technical Staff > >Draper > >555 Technology Square, Rm. 2242 > >Cambridge, MA 02139-3563 > >Phone: (617) 258-4023<tel:%28617%29%20258-4023> > >Cell: (617) 352-1700<tel:%28617%29%20258-4023> > >Email: jpo...@draper.com<mailto:jpo...@draper.com> > >Participate in Operation XDATA: http://xdataonline.com! > > > > > > > >From: Ted Dunning [mailto:ted.dunn...@gmail.com] > >Sent: Tuesday, May 24, 2016 9:44 AM > >To: general@incubator.apache.org > >Cc: Poore, Joshua C. <jpo...@draper.com> > >Subject: Re: [DISCUSS] Accept SensSoft into the Incubator > > > > > >This looks like an excellent project. > > > >How likely is it that it will be able to survive a hypothetical loss of > DARPA funding? > > > > > > > >On Mon, May 23, 2016 at 3:00 PM, lewis john mcgibbney <lewi...@apache.org > <mailto:lewi...@apache.org>> wrote: > >Hi general@, > >I would like to open a DISCUSS thread on the topic of accepting The > >Software as a Sensor™ (SensSoft < > https://wiki.apache.org/incubator/SensSoft>) > >Project into the Incubator. I am CC'ing Joshua Poore from the Charles > Stark > >Draper Laboratory, Inc. who we have been working with to build community > >around a kick-ass set of software projects under the SensSoft umbrella. > >At this stage we would very much appreciate critical feedback from > general@ > >community. > >We are also open to mentors who may have an interest in the project > >proposal. > >The proposal is pasted below. > >Thanks in advance, > >Lewis > > > >= SensSoft Proposal = > > > >== Abstract == > >The Software as a Sensor™ (SensSoft) Project offers an open-source > (ALv2.0) > >software tool usability testing platform. It includes a number of > >components that work together to provide a platform for collecting data > >about user interactions with software tools, as well as archiving, > >analyzing and visualizing that data. Additional components allow for > >conducting web-based experiments in order to capture this data within a > >larger experimental framework for formal user testing. These components > >currently support Java Script-based web applications, although the schema > >for “logging” user interactions can support mobile and desktop > >applications, as well. Collectively, the Software as a Sensor Project > >provides an open source platform for assessing how users interacted with > >technology, not just collecting what they interacted with. > > > >== Proposal == > >The Software as a Sensor™ Project is a next-generation platform for > >analyzing how individuals and groups of people make use of software tools > >to perform tasks or interact with other systems. It is composed of a > number > >of integrated components: > > * User Analytic Logging Engine (User ALE) refers to a simple Application > >Program Interface (API) and backend infrastructure. User ALE provides > >“instrumentation” for software tools, such that each user interaction > >within the application can be logged, and sent as a JSON message to an > >Elasticsearch/Logstash/Kibana (Elastic Stack) backend. > > * The API provides a robust schema that makes user activities human > >readable, and provides an interpretive context for understanding that > >activity’s functional relevance within the application. The schema > provides > >highly granular information best suited for advanced analytics. This > >hierarchical schema is as follows: > > * Element Group: App features that share function (e.g., map group) > > * Element Sub: Specific App feature (e.g., map tiles) > > * Element Type: Category of feature (e.g., map) > > * Element ID: [attribute] id > > * Activity: Human imposed label (e.g., “search”) > > * Action: Event class (e.g., zoom, hover, click) > > * The API can either be manually embedded in the app source code, or > >implemented automatically by inserting a script tag in the source code. > > * Users can either setup up their own Elastic stack instance, or use > >Vagrant, a virtualization environment, to deploy a fully configured > Elastic > >stack instance to ship and ingest user activity logs and visualize their > >log data with Kibana. > > * RESTful APIs allow other services to access logs directly from > >Elasticsearch. > > * User ALE allows adopters to own the data they collect from users > >outright, and utilize it as they see fit. > > * Distill is an analytics stack for processing user activity logs > >collected through User ALE. Distill is fully implemented in Python, > >dependent on graph-tool to support graph analytics and other external > >python libraries to query Elasticsearch. The two principle functions of > >Distill are segmentation and graph analytics: > > * Segmentation allows for partitioning of the available data along > >multiple axes. Subsets of log data can be selected via their attributes in > >User ALE (e.g. Element Group or Activity), and by users/sessions. Distill > >also has the capability to ingest and segment data by additional > attributes > >collected through other channels (e.g. survey data, demographics).This > >allows adopters to focus their analysis of log data on precisely the > >attributes of their app (or users) they care most about. > > * Distill’s usage metrics are derived from a probabilistic > >representation of the time series of users’ interactions with the elements > >of the application. A directed network is constructed from the > >representation, and metrics from graph theory (e.g. betweenness > centrality, > >in/out-degree of nodes) are derived from the structure. These metrics > >provide adopters ways of understanding how different facets of the app are > >used together, and they capture canonical usage patterns of their > >application. This broad analytic framework provides adopters a way to > >develop and utilize their own metrics > > * The Test Application Portal (TAP) provides a single, user-friendly > >interface to Software as a Sensor™ Project components, including > >visualization functionality for Distill Outputs leveraging Django, React, > >and D3.js. It has two key functions: > > * It allows adopters to register apps, providing metadata regarding > >location, app name, version, etc., as well as permissions regarding who > can > >access user data. This information is propagated to all other components > of > >the larger system. > > * The portal also stages visualization libraries that make calls to > >Distill. This allows adopters to analyze their data as they wish to; it’s > >“dashboard” feel provides a way to customize their views with > >adopter-generated widgets (e.g., D3 libraries) beyond what is included in > >the initial open source offering. > > * The Subject Tracking and Online User Testing (STOUT) application is an > >optional component that turns Software as a Sensor™ Technology into a > >research/experimentation enterprise. Designed for psychologists and HCI/UX > >researchers, STOUT allows comprehensive human subjects data protection, > >tracking, and tasking for formal research on software tools. STOUT is > >primarily python, with Django back-end for authentication, permissions, > and > >tracking, MongoDB for databasing, and D3 for visualization. STOUT includes > >a number of key features: > > * Participants can register in studies of software tools using their > own > >preferred credentials. As part of registration, participants can be > >directed through human subjects review board compliant consent forms > before > >study enrollment. > > * STOUT stores URLs to web/network accessible software tools as well as > >URLs to third party survey services (e.g., surveymonkey), this allows > >adopters to pair software tools with tasks, and collect survey data and > >comments from participants prior to, during, or following testing with > >software tools. > > * STOUT tracks participants’ progress internally, and by appending a > >unique identifier, and task identifier to URLs. This information can be > >passed to other processes (e.g., User ALE) allowing for disambiguation > >between participants and tasks in experiments on the open web. > > * STOUT supports between and within-subjects experimental designs, with > >random assignment to experimental conditions. This allows for testing > >across different versions of applications. > > * STOUT can also use Django output (e.g., task complete) to automate > >other processes, such as automated polling applications serving 3rd party > >form data APIs (e.g.,SurveyMonkey), and python or R scripts to provide > >automated post-processing on task or survey data. > > * STOUT provides adopters a comprehensive dashboard view of data > >collected and post-processed through its extensions; in addition to user > >enrollment, task completion, and experiment progress metrics, STOUT allows > >adopters to visualize distributions of scores collected from task and > >survey data. > > > >Each component is available through its own repository to support organic > >growth for each component, as well as growth of the whole platform’s > >capabilities. > > > >== Background and Rationale == > >Any tool that people use to accomplish a task can be instrumented; once > >instrumented, those tools can be used to report how they were used to > >perform that task. Software tools are ubiquitous interfaces for people to > >interact with data and other technology that can be instrumented for such > a > >purpose. Tools are different than web pages or simple displays, however; > >they are not simply archives for information. Rather, they are ways of > >interfacing with and manipulating data and other technology. There are > >numerous consumer solutions for understanding how people move through web > >pages and displays (e.g., Google Analytics, Adobe Omniture). There are far > >fewer options for understanding how software tools are used. This requires > >understanding how users integrate a tool’s functionality into usage > >strategies to perform tasks, how users sequence the functionality provided > >them, and deeper knowledge of how users understand the features of > software > >as a cohesive tool. The Software as a Sensor™ Project is designed to > >address this gap, providing the public an agile, cost-efficient solution > >for improving software tool design, implementation, and usability. > > > >== Software as a Sensor™ Project Overview == > > > >{{attachment:userale_figure_1.png}} > > > >Figure 1. User ALE Elastic Back End Schema, with Transfer Protocols. > > > >Funded through the DARPA XDATA program and other sources, the Software as > a > >Sensor™ Project provides an open source (ALv2.0) solution for > instrumenting > >software tools developed for the web so that when users interact with it, > >their behavior is captured. User behavior, or user activities, are > captured > >and time-stamped through a simple application program interface (API) > >called User Analytic Logging Engine (User ALE). User ALE’s key > >differentiator is the schema that it uses to collect information about > user > >activities; it provides sufficient context to understand activities within > >the software tool’s overall functionality. User ALE captures each user > >initiated action, or event (e.g., hover, click, etc.), as a nested action > >within a specific element (e.g., map object, drop down item, etc.), which > >are in turn nested within element groups (e.g., map, drop down list) (see > >Figure 1). This information schema provides sufficient context to > >understand and disambiguate user events from one another. In turn, this > >enables myriad analysis possibilities at different levels of tool design > >and more utility to end-user than commercial services currently offer. > >Once instrumented with User ALE, software tools become human signal > sensors > >in their own right. Most importantly, the data that User ALE collects is > >owned outright by adopters and can be made available to other processes > >through scalable Elastic infrastructure and easy-to-manage Restful APIs. > >Distill is the analytic framework of the Software as a Sensor™ Project, > >providing (at release) segmentation and graph analysis metrics describing > >users’ interactions with the application to adopters. The segmentation > >features allow adopters to focus their analyses of user activity data > based > >on desired data attributes (e.g., certain interactions, elements, etc.), > as > >well as attributes describing the software tool users, if that data was > >also collected. Distill’s usage and usability metrics are derived from a > >representation of users’ sequential interactions with the application as a > >directed graph. This provides an extensible framework for providing > insight > >as to how users integrate the functional components of the application to > >accomplish tasks. > > > >{{attachment:userale_figure_2.png}} > > > >Figure 2. Software as a Sensor™ System Architecture with all components. > > > >The Test Application Portal (TAP) provides a single point of interface for > >adopters of the Software as a Sensor™ project. Through the Portal, > adopters > >can register their applications, providing version data and permissions to > >others for accessing data. The Portal ensures that all components of the > >Software as a Sensor™ Project have the same information. The Portal also > >hosts a number of python D3 visualization libraries, providing adopters > >with a customizable “dashboard” with which to analyze and view user > >activity data, calling analytic processes from Distill. > >Finally, the Subject Tracking and Online User Testing (STOUT) application, > >provides support for HCI/UX researchers that want to collect data from > >users in systematic ways or within experimental designs. STOUT supports > >user registration, anonymization, user tracking, tasking (see Figure 3), > >and data integration from a variety of services. STOUT allows adopters to > >perform human subject review board compliant research studies, and both > >between- and within-subjects designs. Adopters can add tasks, surveys and > >questionnaires through 3rd party services (e.g., SurveyMonkey). STOUT > >tracks users’ progress by passing a unique user IDs to other services, > >allowing researchers to trace progress by passing a unique user IDs to > >other services, allowing researchers to trace form data and User ALE logs > >to specific users and task sets (see Figure 4). > > > >{{attachment:userale_figure_3.png}} > > > >Figure 3. STOUT assigns participants subjects to experimental conditions > >and ensures the correct task sequence. STOUT’s Django back end provides > >data on task completion, this can be used to drive other automation, > >including unlocking different task sequences and/or achievements. > > > >{{attachment:userale_figure_4.png}} > > > >Figure 4. STOUT User Tracking. Anonymized User IDs (hashes) are > >concatenated with unique Task IDs. This “Session ID” is appended to URLs > >(see Highlighted region), custom variable fields, and User ALE, to provide > >and integrated user testing data collection service. > > > >STOUT also provides for data polling from third party services (e.g., > >SurveyMonkey) and integration with python or R scripts for statistical > >processing of data collected through STOUT. D3 visualization libraries > >embedded in STOUT allow adopters to view distributions of quantitative > data > >collected from form data (see Figure 5). > > > >{{attachment:userale_figure_5.png}} > > > >Figure 5. STOUT Visualization. STOUT gives experimenters direct and > >continuous access to automatically processed research data. > > > >== Insights from User Activity Logs == > > > >The Software as a Sensor™ Project provides data collection and analytic > >services for user activities collected during interaction with software > >tools. However, the Software as a Sensor™ Project emerged from years of > >research focused on the development of novel, reliable methods for > >measuring individuals’ cognitive state in a variety of contexts. > >Traditional approaches to assessment in a laboratory setting include > >surveys, questionnaires, and physiology (Poore et al., 2016). Research > >performed as part of the Software as a Sensor™ project has shown that the > >same kind of insights derived from these standard measurement approaches > >can also be derived from users’ behavior. Additionally, we have explored > >insights that can only be gained by analyzing raw behavior collected > >through software interactions (Mariano et al., 2015). The signal > processing > >and algorithmic approaches resulting from this research have been > >integrated into the Distill analytics stack. This means that adopters will > >not be left to discern for themselves how to draw insights from the data > >they gather about their software tools, although they will have the > freedom > >to explore their own methods as well. > >Insights from user activities provided by Distill’s analytics framework > >fall under two categories, broadly classified as functional workflow and > >usage statistics: > >Functional workflow insights tell adopters how user activities are > >connected, providing them with representations of how users integrate the > >application’s features together in time. These insights are informative > for > >understanding the step-by-step process by which users interact with > certain > >facets of a tool. For example, questions like “how are my users, > >constructing plots?” are addressable through workflow analysis. Workflows > >provide granular understanding of process level mechanics and can be > >modeled probabilistically through a directed graph representation of the > >data, and by identification of meaningful sub-sequences of user activities > >actually observed in the population. Metrics derived provide insight about > >the structure and temporal features of these mechanics, and can help > >highlight efficiency problems within workflows. For example, workflow > >analysis could help identify recursive, repetitive behaviors, and might be > >used to define what “floundering” looks like for that particular tool. > >Functional workflow analysis can also support analyses with more breadth. > >Questions like, “how are my users integrating my tools’ features into a > >cohesive whole? Are they relying on the tool as a whole or just using very > >specific parts of it?” Adopters will be able to explore how users think > >about software as cohesive tools and examine if users are relying on > >certain features as central navigation or analytic features. This allows > >for insights into whether tools are designed well enough for users to > >understand that they need to rely on multiple features together. > >Through segmentation, adopters can select the subset of the data -software > >element, action, user demographics, geographic location, etc.- they want > to > >analyze. This will allow them to compare, for example, specific user > >populations against one another in terms of how they integrate software > >functionality. Importantly, the graph-based analytics approach provides a > >flexible representation of the time series data that can capture and > >quantify canonical usage patterns, enabling direct comparisons between > >users based on attributes of interest. Other modeling approaches have been > >utilized to explore similar insights and may be integrated at a later date > >(Mariano, et al., 2015). > >Usage statistics derive metrics from simple frequentist approaches to > >understanding, coarsely, how much users are actually using applications. > >This is different from simple “traffic” metrics, however, which assess how > >many users are navigating to a page or tool. Rather usage data provides > >insight on how much raw effort (e.g., number of activities) is being > >expended while users are interacting with the application. This provides > >deeper insight into discriminating “visitors” from “users” of software > >tools. Moreover, given the information schema User ALE provides, adopters > >will be able to delve into usage metrics related to specific facets of > >their application. > >Given these insights, different sets of adopters—software developers, > >HCI/UX researchers, and project managers—may utilize The Software as a > >Sensor™ Project for a variety different use cases, which may include: > > * Testing to see if users are interacting with software tools in expected > >or unexpected ways. > > * Understanding how much users are using different facets of different > >features in service of planning future developments. > > * Gaining additional context for translating user/customer comments into > >actionable software fixes. > > * Understanding which features users have trouble integrating to guide > >decisions on how to allocate resources to further documentation. > > * Understanding the impact that new developments have on usability from > >version to version. > > * Market research on how users make use of competitors’ applications to > >guide decisions on how to build discriminating software tools. > > * General research on Human Computer Interaction in service of refining > UX > >and design principles. > > * Psychological science research using software as data collection > >platforms for cognitive tasks. > > > >== Differentiators == > > > >The Software as a Sensor™ Project is ultimately designed to address the > >wide gaps between current best practices in software user testing and > >trends toward agile software development practices. Like much of the > >applied psychological sciences, user testing methods generally borrow > >heavily from basic research methods. These methods are designed to make > >data collection systematic and remove extraneous influences on test > >conditions. However, this usually means removing what we test from > dynamic, > >noisy—real-life—environments. The Software as a Sensor™ Project is > designed > >to allow for the same kind of systematic data collection that we expect in > >the laboratory, but in real-life software environments, by making software > >environments data collection platforms. In doing so, we aim to not only > >collect data from more realistic environments, and use-cases, but also to > >integrate the test enterprise into agile software development process. > >Our vision for The Software as a Sensor™ Project is that it provides > >software developers, HCI/UX researchers, and project managers a mechanism > >for continuous, iterative usability testing for software tools in a way > >that supports the flow (and schedule) of modern software development > >practices—Iterative, Waterfall, Spiral, and Agile. This is enabled by a > few > >discriminating facets: > > > >{{attachment:userale_figure_6.png}} > > > >Figure 6. Version to Version Testing for Agile, Iterative Software > >Development Methods. The Software as a Sensor™ Project enables new methods > >for collecting large amounts of data on software tools, deriving insights > >rapidly to inject into subsequent iterations > > > > * Insights enabling software tool usability assessment and improvement > can > >be inferred directly from interactions with the tool in “real-world” > >environments. This is a sea-change in thinking compared to canonical > >laboratory approaches that seek to artificially isolate extraneous > >influences on the user and the software. The Software as a Sensor™ Project > >enables large scale, remote, opportunities for data collection with > minimal > >investment and no expensive lab equipment (or laboratory training). This > >allows adopters to see how users will interact with their technology in > >their places of work, at home, etc. > > > > * Insights are traceable to the software itself. Traditionally laboratory > >measures—questionnaires, interviews, and physiology—collect data that is > >convenient for making inferences about psychological states. However, it > is > >notoriously difficult to translate this data into actionable “get-well” > >strategies in technology development. User ALE’s information schema is > >specifically designed to dissect user interaction within the terminology > of > >application design, providing a familiar nomenclature for software > >developers to interpret findings with. > > > > * Granular data collection enables advanced modeling and analytics. User > >ALE’s information schema dissects user interaction by giving context to > >activity within the functional architecture of software tools. Treating > >each time-series of user activity as a set of events nested within > >functional components provides sufficient information for a variety of > >modeling approaches that can be used to understand user states (e.g., > >engagement and cognitive load), user workflows (e.g., sub-sequences), and > >users’ mental models of how software tool features can be integrated (in > >time) to perform tasks. In contrast, commercial services such as Google > >Analytics and Adobe Analytics (Omniture) provide very sparse options for > >describing events. They generally advocate for using “boiler plate” event > >sets that are more suited to capturing count data for interactions with > >specific content (e.g., videos, music, banners) and workflows through > >“marketplace” like pages. User ALE provides content agnostic approaches > for > >capturing user activities by letting adopters label them in domain > specific > >ways that give them context. This provides a means by which identical user > >activities (e.g. click, select, etc.) can be disambiguated from each other > >based on which functional sub-component of the tool they have been > assigned > >to. > > > > * Adopter-generated content, analytics and data ownership. The Software > as > >a Sensor™ Project is a set of open-source products built from other > >open-source products. This project will allow adopters to generate their > >own content easily, using open source analytics and visualization > >capabilities. By design, we also allow adopters to collect and manage > their > >own data with support from widely used open source data architectures > >(e.g., Elastic). This means that adopters will not have to pay for > >additional content that they can develop themselves to make use of the > >service, and do not have to expose their data to third party commercial > >services. This is useful for highly proprietary software tools that are > >designed to make use of sensitive data, or are themselves sensitive. > > > >== Current Status == > > > >All components of the Software as a Sensor™ Project were originally > >designed and developed by Draper as part of DARPA’s XDATA project, > although > >User ALE is being used on other funded R&D projects, including DARPA > >RSPACE, AFRL project, and Draper internally funded projects. > >Currently, only User ALE is publically available, however, the Portal, > >Distill, and STOUT will be publically available in the May/June 2016 > >time-frame. The last major release of User ALE was May, 2015. All > >components are currently maintained in separate repositories through > GitHub > >(github.com/draperlaboratory<http://github.com/draperlaboratory>). > >Currently, only software tools developed with Javascript are supported. > >However, we are currently working on pythonQT implementations for User ALE > >that will support many desktop applications. > > > >== Meritocracy == > >The current developers are familiar with meritocratic open source > >development at Apache. Apache was chosen specifically because we want to > >encourage this style of development for the project. > > > >== Community == > >The Software as a Sensor™ Project is new and our community is not yet > >established. However, community building and publicity is a major thrust. > >Our technology is generating interest within industry, particularly in the > >HCI/UX community, both Aptima and Charles River Analytics, for example are > >interested in being adopters. We have also begun publicizing the project > to > >software development companies and universities, recently hosting a public > >focus group for Boston, MA area companies. > >We are also developing communities of interested within the DoD and > >Intelligence community. The NGA Xperience Lab has expressed interest in > >becoming a transition partner as has the Navy’s HCIL group. We are also > >aggressively pursuing adopters at AFRL’s Human Performance Wing, Analyst > >Test Bed. > >During incubation, we will explicitly seek to increase our adoption, > >including academic research, industry, and other end users interested in > >usability research. > > > >== Core Developers == > >The current set of core developers is relatively small, but includes > Draper > >full-time staff. Community management will very likely be distributed > >across a few full-time staff that have been with the project for at least > 2 > >years. Core personnel can be found on our website: > >http://www.draper.com/softwareasasensor > > > >== Alignment == > >The Software as a Sensor™ Project is currently Copyright (c) 2015, 2016 > The > >Charles Stark Draper Laboratory, Inc. All rights reserved and licensed > >under Apache v2.0. > > > >== Known Risks == > > > >=== Orphaned products === > >There are currently no orphaned products. Each component of The Software > as > >a Sensor™ Project has roughly 1-2 dedicated staff, and there is > substantial > >collaboration between projects. > > > >=== Inexperience with Open Source === > >Draper has a number of open source software projects available through > >www.github.com/draperlaboratory<http://www.github.com/draperlaboratory>. > > > >== Relationships with Other Apache Products == > >Software as a Sensor™ Project does not currently have any dependences on > >Apache Products. We are also interested in coordinating with other > projects > >including Usergrid, and others involving data processing at large scales, > >time-series analysis and ETL processes. > > > >== Developers == > >The Software as a Sensor™ Project is primarily funded through contract > >work. There are currently no “dedicated” developers, however, the same > core > >team does work will continue work on the project across different > contracts > >that support different features. We do intend to maintain a core set of > key > >personnel engaged in community development and maintenance—in the future > >this may mean dedicated developers funded internally to support the > >project, however, the project is tied to business development strategy to > >maintain funding into various facets of the project. > > > >== Documentation == > >Documentation is available through Github; each repository under the > >Software as a Sensor™ Project has documentation available through wiki’s > >attached to the repositories. > > > >== Initial Source == > >Current source resides at Github: > > * https://github.com/draperlaboratory/user-ale (User ALE) > > * https://github.com/draperlaboratory/distill (Distill) > > * https://github.com/draperlaboratory/stout (STOUT and Extensions) > > * https://github.com/draperlaboratory/ > > > >== External Dependencies == > >Each component of the Software as a Sensor™ Project has its own > >dependencies. Documentation will be available for integrating them. > > > >=== User ALE === > > * Elasticsearch: https://www.elastic.co/ > > * Logstash: https://www.elastic.co/products/logstash > > * Kibana (optional): https://www.elastic.co/products/kibana > >=== STOUT === > > * Django: https://www.djangoproject.com/ > > * django-axes > > * django-custom-user > > * django-extensions > > * Elasticsearch: https://www.elastic.co/ > > * Gunicorn: http://gunicorn.org/ > > * MySQL-python: https://pypi.python.org/pypi/MySQL-python > > * Numpy: http://www.numpy.org/ > > * Pandas: http://pandas.pydata.org/ > > * psycopg2: http://initd.org/psycopg/ > > * pycrypto: https://www.dlitz.net/software/pycrypto/ > > * pymongo: https://api.mongodb.org/python/current/ > > * python-dateutil: https://labix.org/python-dateutil > > * pytz: https://pypi.python.org/pypi/pytz/ > > * requests: http://docs.python-requests.org/en/master/ > > * six: https://pypi.python.org/pypi/six > > * urllib3: https://pypi.python.org/pypi/urllib3 > > * mongoDB: https://www.mongodb.org/ > > * R (optional): https://www.r-project.org/ > >=== Distill === > > * Flask: http://flask.pocoo.org/ > > * Elasticsearch-dsl: https://github.com/elastic/elasticsearch-dsl-py > > * graph-tool: https://git.skewed.de/count0/graph-tool > > * OpenMp: http://openmp.org/wp/ > > * pandas: http://pandas.pydata.org/ > > * numpy: http://www.numpy.org/ > > * scipy: http://www.numpy.org/ > >=== Portal === > > * Django: https://www.djangoproject.com/ > > * React: https://facebook.github.io/react/ > > * D3.js: https://d3js.org/ > > > >=== GNU GPL 2 === > > > > > >=== LGPL 2.1 === > > > > > >=== Apache 2.0 === > > > > > >=== GNU GPL === > > > > > >== Required Resources == > > * Mailing Lists > > * priv...@senssoft.incubator.apache.org<mailto: > priv...@senssoft.incubator.apache.org> > > * d...@senssoft.incubator.apache.org<mailto: > d...@senssoft.incubator.apache.org> > > * comm...@senssoft.incubator.apache.org<mailto: > comm...@senssoft.incubator.apache.org> > > > > * Git Repos > > * https://git-wip-us.apache.org/repos/asf/User-ALE.git > > * https://git-wip-us.apache.org/repos/asf/STOUT.git > > * https://git-wip-us.apache.org/repos/asf/DISTILL.git > > * https://git-wip-us.apache.org/repos/asf/TAP.git > > > > * Issue Tracking > > * JIRA SensSoft (SENSSOFT) > > > > * Continuous Integration > > * Jenkins builds on https://builds.apache.org/ > > > > * Web > > * http://SoftwareasaSensor.incubator.apache.org/ > > * wiki at http://cwiki.apache.org > > > >== Initial Committers == > >The following is a list of the planned initial Apache committers (the > >active subset of the committers for the current repository on Github). > > > > * Joshua Poore (jpo...@draper.com<mailto:jpo...@draper.com>) > > * Laura Mariano (lmari...@draper.com<mailto:lmari...@draper.com>) > > * Clayton Gimenez (cgime...@draper.com<mailto:cgime...@draper.com>) > > * Alex Ford (af...@draper.com<mailto:af...@draper.com>) > > * Steve York (sy...@draper.com<mailto:sy...@draper.com>) > > * Fei Sun (f...@draper.com<mailto:f...@draper.com>) > > * Michelle Beard (mbe...@draper.com<mailto:mbe...@draper.com>) > > * Robert Foley (rfo...@draper.com<mailto:rfo...@draper.com>) > > * Kyle Finley (kfin...@draper.com<mailto:kfin...@draper.com>) > > * Lewis John McGibbney (lewi...@apache.org<mailto:lewi...@apache.org>) > > > >== Affiliations == > > * Draper > > * Joshua Poore (jpo...@draper.com<mailto:jpo...@draper.com>) > > * Laura Mariano (lmari...@draper.com<mailto:lmari...@draper.com>) > > * Clayton Gimenez (cgime...@draper.com<mailto:cgime...@draper.com>) > > * Alex Ford (af...@draper.com<mailto:af...@draper.com>) > > * Steve York (sy...@draper.com<mailto:sy...@draper.com>) > > * Fei Sun (f...@draper.com<mailto:f...@draper.com>) > > * Michelle Beard (mbe...@draper.com<mailto:mbe...@draper.com>) > > * Robert Foley (rfo...@draper.com<mailto:rfo...@draper.com>) > > * Kyle Finley (kfin...@draper.com<mailto:kfin...@draper.com>) > > > > * NASA JPL > > * Lewis John McGibbney (lewi...@apache.org<mailto:lewi...@apache.org>) > > > >== Sponsors == > > > >=== Champion === > > * Lewis McGibbney (NASA/JPL) > > > >=== Nominated Mentors === > > * Paul Ramirez (NASA/JPL) > > * Lewis John McGibbney (NASA/JPL) > > * Chris Mattmann (NASA/JPL) > > > >== Sponsoring Entity == > >The Apache Incubator > > > >== References == > > > >Mariano, L. J., Poore, J. C., Krum, D. M., Schwartz, J. L., Coskren, W. > D., > >& Jones, E. M. (2015). Modeling Strategic Use of Human Computer Interfaces > >with Novel Hidden Markov Models. [Methods]. Frontiers in Psychology, 6. > >doi: 10.3389/fpsyg.2015.00919 > >Poore, J., Webb, A., Cunha, M., Mariano, L., Chapell, D., Coskren, M., & > >Schwartz, J. (2016). Operationalizing Engagement with Multimedia as User > >Coherence with Context. IEEE Transactions on Affective Computing, PP(99), > >1-1. doi: 10.1109/taffc.2015.2512867 > > > >________________________________ > >Notice: This email and any attachments may contain proprietary (Draper > non-public) and/or export-controlled information of Draper. If you are not > the intended recipient of this email, please immediately notify the sender > by replying to this email and immediately destroy all copies of this email. > >________________________________ >
