The latest generation of WWW tools and services enables Web users to generate applications that combine content from multiple sources. This type of Web application is referred to as a mashup. Many of the tools for constructing mashups rely on a widget paradigm, where users must select, customize, and connect widgets to build the desired application. While this approach does not require programming, the users must still understand programming concepts to successfully create a mashup. As a result, they are put off by the time, effort, and expertise needed to build a mashup. In this article, we describe our programming-by-demonstration approach to building mashup by example. Instead of requiring a user to select and customize a set of widgets, the user simply demonstrates the integration task by example. Our approach addresses the problems of extracting data from Web sources, cleaning and modeling the extracted data, and integrating the data across sources. We implemented these ideas in a system called Karma, and evaluated Karma on a set of 23 users. The results show that, compared to other mashup construction tools, Karma allows more of the users to successfully build mashups and makes it possible to build these mashups significantly faster compared to using a widget-based approach.

Creating a travel plan is not a trivial task, because it often involves multiple steps: selecting places to go, verifying if these places are open, arranging the order of visits, approximating the total cost of the trip, and figuring out the transportation. In this paper, we present an intelligent travel planning system called Pi-Pe. Pi-Pe uses artificial intelligence techniques to create a trip schedule automatically based on locations selected by users. Pi-Pe arranges the order of visit, compute the trip cost, and show the driving direction between each attraction. It also scales the visit time of each attraction dynamically based on the total time available. Pi-Pe is available as a web application and can also be accessed via IPhone. Preliminary user studies and field test experiences show that Pi-Pe helps plan trip faster and is easy to use even to an untrained person.

Creating a Mashup, a web application that integrates data from multiple web sources to provide a unique service, involves solving multiple problems, such as extracting data from multiple web sources, cleaning it, and combining it together. Existing work relies on a widget paradigm where users address those problems during a Mashup building process by selecting, customizing, and connecting widgets together. While these systems claim that their users do not have to write a single line of code, merely abstracting programming methods into widgets has several disadvantages. First, as the number of widgets increases to support more operations, locating the right widget for the task can be confusing and time consuming. Second, customizing and connecting these widgets usually requires users to understand programming concepts. In this paper, we present a Mashup building approach that (a) combines most problem areas in Mashup building into a unified interactive framework that requires no widgets, and (b) allows users with no programming background to easily create Mashups by example.

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.

Integrating and mining data from different web sources can make end-users well-informed when they make decisions. One of many limitations that bars end-users from taking advantages of such process is the complexity in each of the steps required to gather, integrate, monitor, and mine data from different websites. We present the idea of combining the data integration, monitoring, and mining as one single process in the form of an intelligent assistant that guides end-users to specify their mining tasks by just answering questions. This easy-to-use approach, which trades off complexity in terms of available operations with the ease of use, has the ability to provide interesting insight into the data that would requires days of human effort to gather, combine, and mine manually from the web.

We present a question-answering approach where a user without any programming skills can build information agents by simply answering a series of questions. These resulting agents can perform fairly complex tasks that involve retrieving, filtering integrating and monitoring data from online sources. We evaluated our approach to building agents, which is implemented in a system called the Agent Wizard, by re-implementing a set of agents for monitoring travel that originally took four programmers roughly four days to implement. Using the Agent Wizard, the entire set of agents can be implemented in under 35 minutes.

As product prices become increasingly available on the World Wide Web, consumers attempt to understand how corporations vary these prices over time. However, corporations change prices based on proprietary algorithms and hidden variables (e.g., the number of unsold seats on a flight). Is it possible to develop data mining techniques that will enable consumers to predict price changes under these conditions?This paper reports on a pilot study in the domain of airline ticket prices where we recorded over 12,000 price observations over a 41 day period. When trained on this data, Hamlet --- our multi-strategy data mining algorithm --- generated a predictive model that saved 341 simulated passengers $198,074 by advising them when to buy and when to postpone ticket purchases. Remarkably, a clairvoyant algorithm with complete knowledge of future prices could save at most $320,572 in our simulation, thus HAMLET's savings were 61.8% of optimal. The algorithm's savings of $198,074 represents an average savings of 23.8% for the 341 passengers for whom savings are possible. Overall, HAMLET saved 4.4% of the ticket price averaged over the entire set of 4,488 simulated passengers. Our pilot study suggests that mining of price data available over the web has the potential to save consumers substantial sums of money per annum.