This paper presents a novel approach to the design of Human Machine Interface (HMI) systems to be adopted in the supervision of industrial plants. We describe how well known technologies and practices can be transferred from internet-based architectures to embedded systems. We propose a mix of technologies that can be fruitfully used in the implementation of HMI architectures and provide a sketch of the design of a real industrial HMI system that exploit internet communication protocols and Web-based architectures. A set of new features can be achieved thanks to this architecture, such as application adaptivity, interface personalization, control remotization, and multi-channel notification. Finally, we evaluate the resulting platform in terms of performance, reliability, and usability.@en

User models have been defined since the 1980s, mainly for the purpose of building context-based, user-adaptive applications. However, the advent of social networked media, serious games, and crowdsourcing/human computation platforms calls for a more pervasive notion of user model, capable of representing the multiple facets of social users and performers, including their social ties, interests, capabilities, activity history, and topical affinities. In this paper, we define a comprehensive model able to cater for all the aspects relevant for applications involving social networks and human computation; we capitalize on existing social user models and content description models, enhancing them with novel models for human computation and gaming activities representation. Finally, we report on our experiences in adopting the proposed model in the design and implementation of three socially enabled human computation platforms.@en

An increasing number of open data sets is becoming available on the Web as Linked Data (LD), many efforts has been devoted to show the potential of LD applications from the technical point of view. However, less attention has been paid to the analysis of the information seeking requirements from the user point of view. In this paper we examine the Information Seeking Process and we propose a general framework that address all its requirements in the context of LD-based applications. We support seamless integration of both Linked and non-Linked data sources and we allow designers to define complex, rank-aware result construction and exploration rules based on rank aggregation and multiple many-to-many data navigation.@en

The Cubrik Project is an Integrated Project of the 7th Frame- work Programme that aims at contributing to the multimedia search domain by opening the architecture of multimedia search engines to the integration of open source and third party content annotation and query processing components, and by exploiting the contribution of humans and communities in all the phases of multimedia search, from content processing to query processing and relevance feedback processing. The CUBRIK presentation will showcase the architectural concept and scientific background of the project and demonstrate an initial scenario of human-enhanced content and query processing pipeline.@en

As the quantity of software artifacts, mainly source code and software models, stored in repositories increases, the need for their efficient search becomes more important. In this paper we propose content-based query (a.k.a query-by-example) approach for searching software model repositories, in order to retrieve significant models or model fragments. The query-by-example search conveys the user need in form of a model or pattern specified in a coarse way. Our approach incorporates analysis and indexing of models using textual information retrieval techniques, which exploit the knowledge of the metamodel the models conform to. This allows us to explore different segmentation granularities on models and different indexing techniques ranging from simple bag of words, to index structures which integrate metamodel information. We detail the proposed theoretical framework, the implementation of the method upon open-source architectures, and we discuss the results of our experiments upon a public dataset of UML models.

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User models have been defined since the '80s, mainly for the purpose of building context-based, user-adaptive applications. However, the advent of social networked media, serious games, and crowdsourcing platforms calls for a more pervasive notion of user model, capable of representing the multiple facets of a social user, including his social ties, capabilities, activity history, and topical affinities. In this paper, we overview several user models proposed recently to address the platform-independent representation of users embedded in a social context, and discuss the features of the CUbRIK user model, which is designed to support multi-platform human computation applications where users are called as collaborators in the resolution of complex tasks found in the multimedia information retrieval field.

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This paper introduces a conceptual and architectural frame- work for addressing the design, execution and verification of tasks by a crowd of performers. The proposed framework is substantiated by an ongoing application to a problem of trademark logo detection in video collections. Preliminary results show that the contribution of crowds can improve the recall of state-of-the-art traditional algorithms, with no loss in terms of precision. However, task-to-executor matching, as expected, has an important influence on the task performance.@en

Multi-domain search answers to queries spanning multiple entities, like "Find a hotel in Milan close to a concert venue, a museum and a good restaurant", by producing ranked sets of entity combinations that maximize relevance, measured by a function expressing the user's preferences. Due to the combinatorial nature of results, good entity instances (e.g., five stars hotels) tend to appear repeatedly in top-ranked combinations. To improve the quality of the result set, it is important to balance relevance with diversity, which promotes different, yet almost equally relevant, entities in the top-k combinations. This paper explores two different notions of diversity for multi-domain result sets, compares experimentally alternative algorithms for the trade-off between relevance and diversity, and performs a user study for evaluating the utility of diversification in multi-domain queries.

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Model Driven Development may attain substantial productivity gains by exploiting a high level of reuse, across the projects of a same organization or public model repositories. For reuse to take place, developers must be able to perform effective searches across vast collections of models, locate model fragments of potential interest, evaluate the usefulness of the retrieved artifacts and eventually incorporate them in their projects. Given the variety of Web modeling languages, from general purpose to domain specific, from computation independent to platform independent, it is important to implement a search framework capable of harnessing the power of models and of flexibly adapting to the syntax and semantics of the modeling language. In this paper, we explore the use of graph-based similarity search as a tool for expressing queries over model repositories, uniformly represented as collections of labeled graphs. We discuss how the search approach can be parametrized and the impact of the parameters on the perceived quality of the search results.

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Search systems are becoming increasingly sophisticated in their capacity of building results that are not mere lists of documents but articulated sets of concepts retrieved from different domains. As the search result sets exhibit more structure, techniques are required to visualize the retrieved objects in a way that facilitates the immediate understanding of their properties and relationships. This paper investigates the use of models to represent both result sets and visualization spaces, and of model-to-model transformations to dynamically suggest an optimized result visualization for multi-domain search.@en

Modern software project repositories provide support for both source code and design models that describe in details the data structure, behavior, and components of an application. We propose a graph matching-based technique between software models to address content-based query (a.k.a., query by example) on project repositories so as to retrieve significant model fragments for reuse. This can be extremely valuable in a scenario where the designer has a rough idea of the model or pattern he needs, he quickly sketches a coarse schema, and wants to retrieve projects that contain matching patterns (with all the details in place). Our approach encompasses the transformation of models into suitable graphs, the definition of a similarity function and an implementation within a search engine platform. In this paper we present the graph matching approach of the query model against the model repository and we evaluate different configurations of the similarity function.@en

We demonstrate the Search Computing framework for multi-domain queries upon ranked data collected from Web sources. Search Computing answers to queries like "Find a good Jazz concert close to a specified location, a good restaurant and a hotel at walking distance" and fills the gap between generic and domain-specific search engines, by proposing new methods, techniques, interfaces, and tools for building search-based applications spanning multiple data services. The main enabling technology is an execution engine supporting methods for rank-join execution upon ranked data sources, abstracted and wrapped by means of a unifying service model. The demo walks through the interface for formulating multi-domain queries and follows the steps of the query engine that builds the result, with the help of run-time monitors that clearly explain the system's behavior. Once results are extracted, the demonstration shows several approaches for visualizing results and exploring the information space.@en

Search Computing (SeCo) aims at building search applications that bridge the gap between general-purpose and vertical search engines. SeCo queries extract ranked information about several interconnected domains, such as "hotels", "restaurants" or "concerts", by interacting with Web data sources which are wrapped as search services; an example of query is: "Find a good Jazz concert close to the user's current location, together with close-by good restaurants and hotels". The SeCo system supports the deployment of search applications, by providing a generic software architecture and the tools for service and query registration, for query formulation and execution, and for result browsing. In this demo paper, we focus on the Liquid Query (LQ) interface which supports the iteration over query formulation, result visualization and query refinement, with commands for perusing the result set, changing the visualization of data based on their type (e.g., geographical or temporal) and interacting with the remote search services. It also supports an exploratory search approach, where the user starts by accessing one data source (e.g., an event listing for finding interesting concerts), then is assisted in progressively joining other correlated sources in an interactive exploration of the search space. The exploration paths can be chosen on the fly and the navigation history can be browsed back and forth for cross-checking the retrieved options.@en

Search computing queries typically address search tasks that go beyond a single interaction. In this paper, we show a query paradigm that supports multi-step, exploratory search over multiple Web data sources. Our paradigm requires users to be aware of searching over "interconnected objects" with given semantics, but each exploration step is simplified as much as possible, by presenting to users at each step simple interfaces, offering some choices that can be supported by the system; choices include moving "forward", by adding new objects to the search, or "backward", by excluding some objects from the search; and the selection and de-selection of displayed results in order to dynamically manipulate the result set. For supporting exploration, we designed a new architectural element, called query orchestrator, which connects the user interface module with the execution engine; the orchestrator maintains the history of the query session and caches query results for reuse at subsequent interactions.@en

Search systems are becoming increasingly sophisticated in their capacity of building result sets that are not mere lists of documents but articulated combinations of concepts retrieved from different domains. This paper investigates the models for the result sets and the visualization spaces, and model-to-model transformations to dynamically suggest optimized visualizations for multi-domain search results.@en

This chapter focuses on the visualization of multi-domain search results. We start by positioning the problem in the recent line of evolution of search engine interfaces, which more and more are capable of mining semantic concepts and associations from text data and presenting them in sophisticated ways that depend on the type of the extracted data. The approach to visualization proposed in search computing extends current practices in several ways: the data to visualize are N-dimensional combinations of objects, with ranking criteria associated both to individual objects and to sets of combinations; object's properties can be classified in several types, for which optimized visualization families are preferred (e.g., timelines for temporal data, maps for geo-located information); combinations may exhibit any number of relevant properties to be displayed, which need to fit to the bi-dimensional presentation space, by emphasizing the most important attributes and de-emphasizing or hiding the less important ones. The visualization problem therefore amounts to deciding the best mapping between the data of the result set and the visualization space.@en

Multi-domain search answers to queries spanning multiple entities, like "Find an affordable house in a city with low criminality index, good schools and medical services", by producing ranked sets of entity combinations that maximize relevance, measured by a function expressing the user's preferences. Due to the combinatorial nature of results, good entity instances (e.g., inexpensive houses) tend to appear repeatedly in top-ranked combinations. To improve the quality of the result set, it is important to balance relevance (i.e., high values of the ranking function) with diversity, which promotes different, yet almost equally relevant, entities in the top-k combinations. This paper explores two different notions of diversity for multi-domain result sets, compares experimentally alternative algorithms for the trade-off between relevance and diversity, and performs a user study for evaluating the utility of diversification in multi-domain queries.@en

Rich Internet Applications (RIAs) have introduced powerful novel functionalities into the Web architecture, borrowed from client-server and desktop applications. The resulting platforms allow designers to improve the user's experience, by exploiting client-side data and computation, bidirectional client-server communication, synchronous and asynchronous events, and rich interface widgets. However, the rapid evolution of RIA technologies challenges the Model-Driven Development methodologies that have been successfully applied in the past decade to traditional Web solutions. This paper illustrates an evolutionary approach for incorporating a wealth of RIA features into an existing Web engineering methodology and notation. The experience demonstrates that it is possible to model RIA application requirements at a high-level using a platform-independent notation, and generate the client-side and server-side code automatically. The resulting approach is evaluated in terms of expressive power, ease of use, and implementability.@en

Search computing focuses on building answers to complex search queries (for example, "Where can I attend an interesting conference in my field near a sunny beach?") by interacting with a constellation of cooperating search services, and using result ranking and joining as the dominant factors for service composition. The service computing paradigm has so far been neutral to the specific features of search applications and services. To address this weakness, search computing advocates a new approach in which search, join, and ranking are the central aspects for service composition.

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