Research Chapters

A. Theory for search computing. Select the best abstractions covering the concepts of conventional vs. search services and their interactions, abstracted as networks of operational nodes. Design basic operations on services and the algorithms to perform such operations, compute time and space complexity of these algorithms.
B. Statistical models for search services. Build statistical estimators of the number and quality of the results produced by services or by operations on services. Methods can be based upon analytical models, simulators, or observation, they can be query independent or query specific.
C. Optimization methods for search computing. Build cost models for search service computations. Design exact and heuristic methods for solving optimization problems in practical contexts.
D. Description abstractions for search services. Expose ranking-specific properties of search services - much in the same way as all other functional and non fun-ctional properties of services - so as to drive search service selection and composition strategies.
E. Language abstractions for search computing. Build languages that enable expressing queries over search services, by incorporating the ranking aspects and strategies for dealing with rankings.
F. Human-computer interfaces for search computing. Build new and original interaction paradigms focused on expressing ranking preferences. User interaction should be light-weight and at the same time user-engaging, linking one request to the next; it should help orchestrators in “resolving” conflicts, e.g. due to incommensurable rankings rendered as partial orders.
G. Semantics for search computing. Merging the results of heterogeneous search services dealing with the “same real-world fact” is inherently difficult, as the search services will not share the same terminology for describing such fact. Thus, the “join” of search services requires solving semantic problems. Search computing research will incorporate some results stemming from automatic reasoning, inference upon ontologies, and semantic Web services.
H. Higher-order rankings for search computing. Higher-order ranking, or the “ranking of rankings”, is essential for selecting and prioritizing search services. This problem is inherently a multi-level one, and can be solved by assigning “ranks” to search engines based on the query domain and then using such ranks in the higher-order orchestration layer.
I. Search computing in the clouds. Search computing systems entail many computing-demanding steps: service discovery and selection, plan optimization, result ranking and merging, interaction with ontological sources, adaptive plan modification. The bulky nature of many steps, which can be assimilated to algebraic operations over graphs, makes search computing a suitable application to be assigned to clouds of computing systems, where nodes are indistinguishable from each other.
J. Managing individual and social searching. Factors such as relating search strategies to user profiling or to past user interactions may significantly improve the perceived quality of each user. Societal recommendation and evaluation is the most critical success factor for most Web applications, including search, as the number of visits is the worldwide used success metric. Thus, individual and societal aspects are key ingredients for search computing.
K. Search computing engineering. Develop a method for specifying, designing, assembling and deploying search computing software applications. The method should separately address service design and service orchestration, where the first activity produces best-suited ingredients to be used (and re-used) by the second activity.
L. Economy of search computing. The success of search computing will depend on the development of an economy of search services. Application developers and data source owners will need suitable business models, based upon advertising schemes, pay-per-query, subscription fees, micro-billing, and so on.
M. Security and privacy of search computing. Search computing raises novel issues about the control of how data is used. For instance, use of a search service could be granted to a service computing application, provided that the service’s owners can trace all queries involving their data and limit the kind of information that is made visible to the queries.