More and more applications require real-time processing of massive, dynamically generated, ordered data; order is an essential factor as it reflects recency or relevance. Semantic technologies risk being unable to meet the needs of such applications, as they are not equipped with the appropriate instruments for answering queries over massive, highly dynamic, ordered data sets. In this vision paper, we argue that some data management techniques should be exported to the context of semantic technologies, by integrating ordering with reasoning, and by using methods which are inspired by stream and rank-aware data management. We systematically explore the problem space, and point both to problems which have been successfully approached and to problems which still need fundamental research, in an attempt to stimulate and guide a paradigm shift in semantic technologies.@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

In this paper we propose the Liquid Query paradigm, to support users in finding responses to multi-domain queries through exploratory information seeking across structured information sources (Web documents, deep Web data, and personal data repositories), wrapped by means of a uniform notion of search service. Liquid Query aims at filling the gap between general-purpose search engines, which are unable to find information spanning multiple topics, and domain-specific search systems, which cannot go beyond their domain limits. The Liquid Query interface consists of interaction primitives that let users pose questions and explore results spanning over multiple sources incrementally, thus getting closer and closer to the sought information. We demonstrate our approach with a prototype built upon the YQL (Yahoo! Query Language) framework.@en

Liquid queries are a flexible tool for information seeking, based on the progressive exploration of the search space; they produce "fluid" results which dynamically adapt to the shape of the query, as a liquid adapts to its container. The liquid query paradigm relies on the SeCo service mart and multi-domain query execution concepts: an expert user selects a priori the service marts relevant to the information seeking task at hand and the connections necessary to join them, and publishes such a definition in the SeCo back-end. The Liquid Query client-side interface consumes the application definition created by the expert and dynamically builds a query interface for the end-user. Such interface allows one to supply keywords to query the pre-configured service marts and offers controls for exploring the combinations computed by the SeCo execution engine. The interaction commands are based on a tabular representation of results and comprise: reordering, clustering, addition or deletion of attributes, addition of extra service marts to the query for specific items in the result set or for the entire result set, request of more results from all services or from selected ones, expansion of details on selected items, and more. The Liquid Query is equipped with multiple data visualization options suited to render multi-domain results and can be instrumented with indicators showing the quality of the result set.@en

Search Computing aims at opening the Web to a new class of search applications, by offering enhanced expressive and computational power. The success of Search Computing, as of any technical advance, will be measured by its impact upon the search industry and market, and this in turn will be highly influenced by reactions of Web users and developers. It is too early to anticipate such reactions - as the technology is still "under construction" - but this chapter attempts a first identification of the possible future players in the development of Search Computing applications, by grossly identifying the roles of "data source publishers" and of "application developers", and by discussing how classical advertising-based models may support the new applications. This chapter also describes the high-level design of the prototyping environment that is currently under development and how the design will support the deployment upon high performance architectures. Finally, we describe advertising as the prevalent business model of the search engines industry, and briefly discuss the options for the evolution of such model in the context of Search Computing.@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.@en

Expert selection is an important aspect of many Web applications, e.g., when they aim at matching contents, tasks or advertisement based on user profiles, possibly retrieved from social networks. This paper focuses on selecting experts within the population of social networks, according to the information about the social activities of their users. We consider the following problem: given an expertise need (expressed for instance as a natural language query) and a set of social network members, who are the most knowledgeable people for addressing that need? We considers social networks both as a source of expertise information and as a route to reach expert users, and define models and methods for evaluating people's expertise by considering their profiles and by tracing their activities in social networks. For matching queries to social resources, we use both text analysis and semantic annotation. An extensive set of experiments shows that the analysis of social activities, social relationships, and socially shared contents helps improving the effectiveness of an expert finding system.@en

In many settings, the human opinion provided by an expert or knowledgeable user can be more useful than factual information retrieved by a search engine. Search systems do not capture the subjective opinions and recommendations of friends, or fresh, online-provided information that require contextual or domain-specific expertise. Search results obtained from conventional search engines can be complemented by crowdsearch, an online interaction with crowds, selected among friends, experts, or people who are presently at a given location; an interplay between conventional and search-based queries can occur, so that the two search methods can support each other. In this paper, we use a model-driven approach for specifying and implementing a crowdsearch application; in particular we define two models: the "Query Task Model", representing the meta-model of the query that is submitted to the crowd and the associated answers; and the "User Interaction Model", showing how the user can interact with the query model to fulfil her needs. Our solution allows for a top-down design approach, from the crowd-search task design, down to the crowd answering system design. Our approach also grants automatic code generation, thus leading to quick prototyping of crowd-search applications.@en

Web users are increasingly relying on social interaction to complete and validate the results of their search activities. While search systems are superior machines to get world-wide information, the opinions collected within friends and expert/local communities can ultimately determine our decisions: human curiosity and creativity is often capable of going much beyond the capabilities of search systems in scouting "interesting" results, or suggesting new, unexpected search directions. Such personalized interaction occurs in most times aside of the search systems and processes, possibly instrumented and mediated by a social network; when such interaction is completed and users resort to the use of search systems, they do it through new queries, loosely related to the previous search or to the social interaction. In this paper we propose CrowdSearcher, a novel search paradigm that embodies crowds as first-class sources for the information seeking process. CrowdSearcher aims at filling the gap between generalized search systems, which operate upon world-wide information - including facts and recommendations as crawled and indexed by computerized systems - with social systems, capable of interacting with real people, in real time, to capture their opinions, suggestions, emotions. The technical contribution of this paper is the discussion of a model and architecture for integrating computerized search with human interaction, by showing how search systems can drive and encapsulate social systems. In particular we show how social platforms, such as Facebook, LinkedIn and Twitter, can be used for crowdsourcing search-related tasks; we demonstrate our approach with several prototypes and we report on our experiment upon real user communities.@en

We demonstrate Liquid Queries, a novel user interaction paradigm for exploratory multi-domain search upon structured information collected from heterogeneous data sources. Liquid Queries support an exploratory search approach by providing a set of interaction primitives for multi-domain 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) and interacting with the remote search services.@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

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

This paper reports the main findings of a panel about trends in search engine interaction, focused upon the use of search engines for performing complex processes. The discussion focuses on the different evolutionary path followed by search engines with respect to other Web and information management solutions, making end users acquainted with the simplistic and never changing keyword-based query paradigm. The analysis delves into the pros and cons of personalization, contextualization, and exploration of Web information, with special attention to the presentation and user interaction aspects. In the end, we also wonder if the keyword-based query paradigm will ever change.@en

This article presents the adaptation of a general search computing framework for exploratory search over Web data as suggested by the specificity of location-based data services. The result is a conceptual model of geographic entities, the spatial functions operating on them, and a special-purpose exploratory interface that lets users search combinations of georeferenced objects directly on a map. Such modifications help the general framework provide ranked extraction of relevant objects and their combinations, custom ranking functions, and cost-based access to location-based services.@en

Search Computing defines a new class of applications, which enable end users to perform exploratory search processes over multi-domain data sources available on the Web. These applications exploit suitable models, supported by a framework, that make it possible for expert users to configure the data sources to be searched and the interfaces for query submission and result visualization, by using for such source and interface configurations mash-up tools which do not require programming. This paper presents Search Computing design process and developer roles, together with the conceptual models that represent the foundation of a model-driven approach to Search Computing.@en

Current search engines do not support queries that require a complex combination of information. Problems such as "Which theatre offers an at least-three-stars action movie in London close to a good Italian restaurant" can only be solved by asking multiple queries, possibly to different search engines, and then manually combining results, thereby performing "data integration in the brain." While searching the Web is the preferred method for accessing information in everyday's practice, users expect that search systems will soon be capable of mastering complex queries. However, combining information requires a drastic change of perspective: a new generation of search computing systems is needed, capable of going beyond the capabilities of current search engines. In this paper we show how search computing should open to modular composition, as many other kinds of software computations. We first motivate our work by describing our vision, and then describe how the challenges of multi-domain search are addressed by a prototype framework, whose internal "anatomy" is disclosed.@en

Exploratory search is an information seeking behavior where users progressively learn about one or more topics of interest; it departs quite radically from traditional keyword-based query paradigms, as it combines querying and browsing of resources, and covers activities such as investigating, evaluating, comparing, and synthesizing retrieved information. In most cases, such activities are enabled by a conceptual description of information in terms of entities and their semantic relationships. Customized Web applications, where few applicative entities and their relationships are embedded within the application logics, typically provide some support to exploratory search, which is, however, specific for a given domain. In this paper, we describe a general-purpose exploratory search framework, i.e., a framework which is neutral to the application logic. Our contribution consists of the formalization of the exploratory search paradigm over Web data sources, accessed by means of services; extracted information is described by means of an entity-relationship schema, which masks the service implementations. Exploratory interaction is supported by a general-purpose user interface including a set of widgets for data exploration, from big tables to atomic tables, visual diagrams, and geographic maps; the user interaction is translated to queries defined in SeCoQL SeCoQL , a SQL-like language and protocol specifically designed for supporting exploratory search over data sources. We illustrate the software architecture of our prototype, which uses the interplay of a query and result management system with an orchestrator, capable of incrementally building queries and of walking through the past navigation history. The distinctive feature of the framework is the ability to extract top solutions, which combine top-ranked entity instances. We evaluate exploratory search from the end-user perspective in the context of a cognitive model for search, by studying the user’s behavior and the effectiveness of exploratory search in terms of quality of results produced by the search process; we also compare the effectiveness of interaction in using our multi-domain search system with the use of various replicas of the system, each acting upon a single domain, and with the use of conventional search engines.@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 defines a new class of applications, which enable end users to perform exploratory search processes over multi-domain data sources available on the Web. These applications exploit suitable software frameworks and models that make it possible for expert users to configure the data sources to be searched and the interfaces for query submission and result visualization. We describe some usage scenarios and the reference architecture for Search Computing systems.@en

n essential aspect for building effective crowdsourcing com- putations is the ability of "controlling the crowd", i.e. of dynamically adapting the behaviour of the crowdsourcing systems as response to the quantity and quality of completed tasks or to the availability and reliability of performers. Most crowdsourcing systems only provide limited and predefined controls; in contrast, we present an approach to crowdsourcing which provides fine-level, powerful and flexible controls. We model each crowdsourcing application as composition of elementary task types and we progressively transform these high level specifications into the features of a reactive execution environment that supports task planning, assignment and completion as well as performer monitoring and exclusion. Controls are specified as active rules on top of data structures which are derived from the model of the application; rules can be added, dropped or modified, thus guaranteeing maximal flexibility with limited effort. We also report on our prototype platform that implements the proposed framework and we show the results of our experimentations with different rule sets, demonstrating how simple changes to the rules can substantially affect time, effort and quality involved in crowdsourcing activities.@en