Mentor: Tom White

Working across multiple programming languages is often a painful experience. De/Serialization of data across languages often requires lengthy boilerplate code prone to failures. Serialization of graphs and cycles is often impossible or hard to accomplish. Additionally, it is radically hard to port software and validate that those ports work correctly.

Fortunately, it doesn't have stay this way. S.IM.PL (Support for Information Mapping between Programming Languages) is an architecture that promises to fix these problems; we'd love to hire a developer to help us implement it in JavaScript. Our lab uses S.IM.PL as the foundation for some of our research projects; when you're done implementing it, you'll be able to use it as a foundation for your own projects!

Goals:

• Support de/serialization of S.IM.PL enhanced JSON object graphs across web services and/or applications, such as the BigSemantics service and the In-Context Expander (ICE)
• Facilitate S.IM.PLer service development and portability on JavaScript-based platforms like Node.js

Deliverables:

Through the course of this project, you will develop a JavaScript-based implementation of S.IM.PL. You'll be able to follow many of the patterns from the reference implementation of S.IM.PL. Your implementation, of course, will also include a robust suite of tests to validate its correctness! Additionally, you'll work on combining S.IM.PL with some of our other tools, such as BigSemantics, to help validate and improve the value of your implementation.

Some of these use S.IM.PL-specific terminology, feel free to read our documentation or contact us for more details.

• S.IM.PL JavaScript Translator (Written in Java)
• Provides facilities to translate classes in languages like Java into JavaScript representations
• Will be leveraged to produce a "skeleton" implementation of S.IM.PL you build upon
• "Proto"-S.IM.PL JavaScript (First phase in implementation)
• Implementations of core functionality in simpl ("Descriptoin" of types)
• Implementations of core mappings between JavaScript types and S.IM.PL's core type system (Scalar types, List types, Map types, and Generics)
• Completing the core of S.IM.PL JavaScript
• Implement JSON serialization (of object graphs)
• Leverage JSON Serialization to boostrap service-driven testing of S.IM.PL
• Implement JSON deserialization
• Write roundtrip test bench, leveraging service testing to validate JSON de/serialization
• Finishing touches, integration with BigSemanticsJavaScript
• Write additional type mappings to help make your implementaiton more complete
• Integrate with BigSemanticsJavaScript
• Validate additional tricky de/serialization cases
• Write additional formats (XML for sure, maybe YAML if you have time?)

The architecture has a decent number of small pieces that, once all implemented, give you a complete, robust cross-language type system. We'll help you through the entire process! All you need to provide are your skills (an understanding of type systems, Javascript, and a bit of Java knowledge to understand the reference implementation), and your desire to develop! For more questions, contact us on IRC (#ecologylab on irc.freenode.net) or send us an email.

Mentor: Rhema Linder

Working across multiple programming languages is often a painful experience. De/Serialization of data across languages often requires lengthy boilerplate code prone to failures. Serialization of graphs and cycles is often impossible or hard to accomplish. Additionally, it is radically hard to port software and validate that those ports work correctly.

Fortunately, it doesn't have stay this way. S.IM.PL (Support for Information Mapping between Programming Languages) is an architecture that promises to fix these problems; we'd love to hire a developer to help us implement it in Python. Our lab uses S.IM.PL as the foundation for some of our research projects; when you're done implementing it, you'll be able to use it as a foundation for your own projects!

Goals:

• Support de/serialization of S.IM.PL enhanced JSON object graphs across Python and non-Python web services and/or applications
• Facilitate S.IM.PLer service development and portability on Python-based platforms like Django

Deliverables:

Through the course of this project, you will develop a JavaScript-based implementation of S.IM.PL. You'll be able to follow many of the patterns from the reference implementation of S.IM.PL. Your implementation, of course, will also include a robust suite of tests to validate its correctness! Additionally, you'll work on combining S.IM.PL with some of our other tools, such as BigSemantics, to help validate and improve the value of your implementation.

Some of these use S.IM.PL-specific terminology, feel free to read our documentation or contact us for more details.

• S.IM.PL Python Translator (Written in Java)
• Provides facilities to translate classes in languages like Java into Python representations
• Will be leveraged to produce a "skeleton" implementation of S.IM.PL you build upon
• "Proto"-S.IM.PL Python (First phase in implementation)
• Implementations of core functionality in simpl ("Descriptoin" of types)
• Implementations of core mappings between Python types and S.IM.PL's core type system (Scalar types, List types, Map types, and Generics)
• Completing the core of S.IM.PL Python
• Implement JSON serialization (of object graphs)
• Leverage JSON Serialization to boostrap service-driven testing of S.IM.PL
• Implement JSON deserialization
• Write roundtrip test bench, leveraging service testing to validate JSON de/serialization
• Finishing touches, integration with BigSemantics
• Write additional type mappings to help make your implementaiton more complete
• Validate additional tricky de/serialization cases
• Write additional formats (XML for sure, maybe YAML if you have time?)

The architecture has a decent number of small pieces that, once all implemented, give you a complete, robust cross-language type system. We'll help you through the entire process! All you need to provide are your skills (an understanding of type systems, Python, and a bit of Java knowledge to understand the reference implementation), and your desire to develop! For more questions, contact us on IRC (#ecologylab on irc.freenode.net) or send us an email.

Mentor: Nic Lupfer

The Managing Web Semantics in JavaScript project focuses on extracting, understanding, and visualizing web semantics(metadata) within a web applications or browser plugins. The deliverables of this project are centered around increasing the stability, functionality, and interoperability of the Metadata Extractor browser plugin and the In-Context Expander (ICE) dynamic interface in HTML5.

The Metadata Extractor is a browser plugin which parses the DOM of web pages as your browse to extract metadata using the BigSemantics meta-metadata language. Currently available for Chrome and Firefox the plugin exposes the extracted metadata to other web or desktop applications through HTML5 data injection on DataTransfer events.

ICE is a dynamic interface which renders web semantics in HTML5. The interface allows for users to browse and navigate to linked web semantics. The student will design and create new dynamic interfaces. Different dynamic interfaces will share significant implementation with ICE but should be novel in how they visualize the data and how the user navigates to connected web semantics.

This project is part of the Interface Ecology Lab's open source BigSemantics toolkit , specifically BigSemantics JavaScript.

Deliverables:

• Metadata Extractor Pt. 1
  • Test cases and testing framework
  • Validate XPath parsing
  • Direct path parsing
• S.IM.PL
  • De/serializing metadata Documents
• BigSemantics
  • Implement / port URL selector
  • URL to MetaMetadata resolution
  • Document comparison & concatenator
• Metadata Extractor Pt. 2
  • Incorporate SIMPL de/serialization
  • Incorporate URL to MetaMetadata resolution using the MMD repository JSON file
• Dynamic Interfaces for Web Semantics (stretch deliverable)
  • Design and create novel dynamic interfaces

Mentor: Andruid Kerne

This project develops popular perspectives in web semantics in order to involve the general public, especially young people, in new experiences of collecting, presenting, and sharing information. Curation is the process of developing a conceptual focus, and selecting and connecting significant information that expresses the chosen concepts. Each REU student assigned to this project will identify a cultural area significant to her / him, and work on the curation of semantic information in this area. Examples of potential popular culture areas include fashion, sports, music, entertainment, skateboarding, DIY, crafts, design, and dance.

The goal of this semantics curation is to make information in these popular culture areas available to people in new ways, to catalyze new expressions, uses, and communities involving the information. Curating semantics involves identifying cultural areas of information on the web that matter to particular groups of people, defining data structures to best represent this information, identifying template-driven web sites that publish data that can be used to instantiate the data structures, and defining XPath and regular expressions that extract information from the web sites. This involves using the Interface Ecology Lab's open source BigSemantics toolkit and ICE presentation tool.

The project involves using social media to coalesce user communities around new visual semantic presentations in the area of popular culture that the student curates.

More than one student can work on this project, assuming that each works on a different area of popular culture. These areas and their associated students can fruitfully intersect.

Deliverables:

• Develop reusable semantic data models for areas of popular culture, using Meta-Metadata.
• Develop selectors and wrappers for specific web sites.
• Build fun tutorial applicationsl / demo in DHTML incorporating ICE and popular culture.
• Alternative dynamic metadata visualizations using D3.
• Produce clear documentation in the BigSemantics Wiki.

Mentor: Andruid Kerne

We need methods to facilitate presenting rich heterogeneous metadata on maps. Rich metadata consists of descriptions and relational associative links, derived from web pages, such as a movie, its actors and their movies, and a historical site, its significant events and artifacts, and other related events, sites, and artifacts.

The Interface Ecology Lab's open source BigSemantics project has developed tools for wrappers that represent the data models for rich metadata, extraction rules for particular web sites and other sources, and hints for presentation. A repository of wrappers covers many popular web sites. BigSemantics also already includes components for presenting this rich metadata in JavaScript, Java, and C# / WinRT. The ICE presentation tool is an example.

This project will develop tools and examples for representing rich metadata instances, each associated with a GPS location, on maps, to be known as rich placemarks. The instances of metadata for places that go on the map could be curated in advance, or collected ad hoc by drag and drop. This will be accomplished by integrating BigSemantics, ICE, and OpenStreetMaps (and perhaps Google Maps).

Deliverables:

• Extend and develop reusable semantic data models for places, using Meta-Metadata.
• Develop selectors and wrappers for specific web sites that present mappable locations.
• Develop an integration of OpenStreetMaps and ICE, which enables interaction with rich place-oriented metadata on a map.
• Build fun tutorial / demo applications in DHTML incorporating ICE with popular culture, such as mashups involving TripAdvisor, Expedia, and WIkipedia.
• Produce clear documentation in the BigSemantics and ICE Wikis.

The developer needs experience with HTML5, JSON, and interaction design. Source code is organized with Git.

Mentor: Yin Qu

This project develops a scalable data storage and indexing backend for the BigSemantics service. The Interface Ecology Lab developed the BigSemantics service as open source software to support extracting, representing and presenting semantic data on the web in desktop, mobile, and cloud applications (BigSemantics Wiki). The developed system will provide the service with a scalable backend to store accessed web pages and extracted semantic data, to improve performance. It will also automatically construct indexes and aggregations on relevant fields. The storage and indexing backend will support critical interactions with semantic data needed for planning and doing disaster response, such as filtering, searching, ranking, and faceted browsing.

The student assigned to this project will need to build on existing techniques to develop the system and connect it to the BigSemantics service, under guidance. This involves learning the code structures and APIs of BigSemantics and the service project developed by the Interface Ecology Lab, designing proper interfaces and APIs to abstract the new module, and invoking the backend data store like CouchDB when needed by the service. By the end of the summer, the student should develop a demo web application that uses the storage and indexing functionalities, as a validation.

The Interface Ecology Lab will provide a virtual Linux environment for the student to develop this server-side system. The resulting software will be included in the BigSemantics service release, available to the public as open source software.

The student will learn the architecture and implementation of modern web-based services, and the use of services in advanced interaction technologies. S/he will work in a team of active developers and researchers, participate in conversations, and involve in the process of producing open source software.

Deliverables:

• Test cases for storing HTML pages, extracted semantics, and file metadata.
• Implementing classes for existing interfaces, to move to CouchDB as the backend storage system.
• Supporting specifying index and facet on wrappers written in meta-metadata.
• Automatically building and updating indexes for fields with index or facets.
• Demo application that uses indexes and facets for viewing metadata.

Mentor: TBD

Deliverables: