On the desktop, an application can expect to control its user interface down to the last pixel, but on the World Wide Web, a content provider has no control over how the client will view the page, once delivered to the browser. This creates an opportunity for end-users who want to automate and customize their web experiences, but the growing complexity of web pages and standards prevents most users from realizing this opportunity. We describe Chickenfoot, a programming system embedded in the Firefox web browser, which enables end-users to automate, customize, and integrate web applications without examining their source code. One way Chickenfoot addresses this goal is a novel technique for identifying page components by keyword pattern matching. We motivate this technique by studying how users name web page components, and present a heuristic keyword matching algorithm that identifies the desired component from the user’s name. ACM Classification: H5.2 [Information interfaces and

p0220 p0225 p0230 Many users are familiar with the interesting but limited functionality of data detector interfaces like Microsoft’s Smart Tags and Google’s AutoLink. In this chapter we significantly expand the breadth and functionality of this type of user interface through the use of large-scale knowledge bases of semantic information. The result is a Web browser that is able to generate personalized semantic hypertext, providing a goal-oriented browsing experience. We present (1) Creo, a programming-by-example system for the Web that allows users to create a general purpose procedure with a single example; and (2) Miro, a data detector that matches the content of a page to high-level user goals. An evaluation with 34 subjects found that they were more efficient using our system, and that the subjects would use features like these if they were integrated into their Web browser. s0010 p0235 p0240

We describe a system, implemented as a browser extension, that enables users to quickly and easily collect, view, and share personal Web content. Our system employs a novel interaction model, which allows a user to specify webpage extraction patterns by interactively selecting webpage elements and applying these patterns to automatically collect similar content. Further, we present a technique for creating visual summaries of the collected information by combining user labeling with predefined layout templates. These summaries are interactive in nature: depending on the behaviors encoded in their templates, they may respond to mouse events, in addition to providing a visual summary. Finally, the summaries can be saved or sent to others to continue the research at another place or time. Informal evaluation shows that our approach works well for popular websites, and that users can quickly learn this interaction model for collecting content from the Web.

The Web is ephemeral. Pages change frequently, and it is nearly impossible to find data or follow a link after the underlying page evolves. We present Zoetrope, a system that enables interaction with the historical Web (pages, links, and embedded data) that would otherwise be lost to time. Using a number of novel interactions, the temporal Web can be manipulated, queried, and analyzed from the context of familar pages. Zoetrope is based on a set of operators for manipulating content streams. We describe these primitives and the associated indexing strategies for handling temporal Web data. They form the basis of Zoetrope and enable our construction of new temporal interactions and visualizations. ACM Classification: H5.2 [Information interfaces and

The magnitude of data available on the web prompts the need for an easy to use query interface that enables users to integrate data from multiple web sources in an intelligent fashion. Past work in the area of databases has resulted in different query interface systems that simplify query formulation. While these approaches reduce the user’s effort to compose queries, the user is still required to pick data sources to use and the interaction is not guaranteed to yield a non-empty result set. We introduce a novel approach that exploits the structure of the relational data source(s) to formulate a set of constraints. These constraints are used in conjunction with partial plans to produce an intelligent query interface that (a) does not require the user to know details about data sources or existing values (b) suggests valid inputs to the user (c) creates consistent queries that always return values. ACM Classification Keywords

We present three new interaction techniques for aiding users in collecting and organizing Web content. First, we demonstrate an interface for creating associations between websites, which facilitate the automatic retrieval of related content. Second, we present an authoring interface that allows users to quickly merge content from many different websites into a uniform and personalized representation, which we call a card. Finally, we introduce a novel search paradigm that leverages the relationships in a card to direct search queries to extract relevant content from multiple Web sources and fill a new series of cards instead of just returning a list of webpage URLs. Preliminary feedback from users is positive and validates our design. ACM Classification H5.2 [Information interfaces and presentation]:

MashMaker is a web-based tool that makes it easy for normal users to create mashups from live data on the internet. Users can query, combine, and explore data, using an interface inspired by spreadsheets and web browsers. Like a spreadsheet, MashMaker mixes program and data and allows ad-hoc unstructured editing of programs. Like a web browser, MashMaker allows users to find the information they are interested in by browsing, rather than writing code, and allows users to bookmark interesting things they find, forming new widgets — reusable mashup fragments. MashMaker is also a modern functional programming language with non-side effecting expressions, higher order functions, and lazy evaluation. We argue that a functional language provides an excellent model to allow users to easily create mashups from web data. In order to cope with this unusual domain, MashMaker contains a number of deviations from normal functional programming languages. Data is live, programs are mixed with data, map and fold operations are described using direct manipulation of data, data is structured like a file-system, and it is possible to write a program largely by browsing around, without having to type or decide in advance what one wants to do.

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Authoring dynamic web pages is an inherently difficult task. We present DESK, an interactive authoring tool that allows the customization of dynamic page generation procedures with no apriori tool-specific skill requirements from authors. Our approach consists of combining Programming By Example (PBE) techniques with an ontology-based representation of knowledge displayed in web pages. DESK acts as a client-side complement of a dynamic web page generation system, PEGASUS, which generates HTML pages from a formally structured domain model and an abstract presentation model. Authorized users can modify the internal presentation model by editing the generated HTML pages with DESK in a WYSIWYG environment. DESK keeps track of all user's actions and exploits the explicitly represented domain semantics to enhance the power of PBE techniques.

In many scenarios, such as emergency response or ad hoc collaboration, it is critical to reduce the overhead in integrating data. Here, the goal is often to rapidly integrate “enough ” data to answer a specific question. Ideally, one could perform the entire process interactively under one unified interface: defining extractors and wrappers for sources, creating a mediated schema, and adding schema mappings — while seeing how these impact the integrated view of the data, and refining the design accordingly. We propose a novel smart copy and paste (SCP) model and architecture for seamlessly combining the design-time and run-time aspects of data integration, and we describe an initial prototype, the CopyCat system. In CopyCat, the user does not need special tools for the different stages of integration: instead, the system watches as the user copies data from applications (including the Web browser) and pastes them into CopyCat’s spreadsheet-like workspace. CopyCat generalizes these actions and presents proposed auto-completions, each with an explanation in the form of provenance. The user provides feedback on these suggestions — through either direct interactions or further copy-and-paste operations — and the system learns from this feedback. This paper provides an overview of our prototype system, and identifies key research challenges in achieving SCP in its full generality. 1.