This paper addresses conceptual modeling and automatic code generation for search engine integration with data intensive Web applications. We have analyzed the similarities (and differences) between IR and database systems to extend an existing domain specific language for data-driven Web applications. The extended Web modeling language specifies the search engine's index schemas based on the data schema of the Web application and uniquely designs the interaction between the database, the Web application, the search engine and users. We also provide an implementation of a CASE tool extension for visual modeling and code generation. Experimentation of the proposed approach has been successfully applied in the context of the COOPER project.@en

Modeling Web query reformulation processes is still an unsolved problem. In this paper we argue that lexical analysis is highly beneficial for this purpose. We propose to use the variation in Query Clarity, as well as the Part-Of-Speech pattern transitions as indicators of user's search actions. Experiments with a log of 2.4 million queries showed our techniques to be more flexible than the current approaches, while also providing us with interesting insights into user's Web behavioral patterns.@en

We propose a novel approach for learning node representations in directed graphs, which maintains separate views or embedding spaces for the two distinct node roles induced by the directionality of the edges. We argue that the previous approaches either fail to encode the edge directionality or their encodings cannot be generalized across tasks. With our simple alternating random walk strategy, we generate role specific vertex neighborhoods and train node embeddings in their corresponding source/target roles while fully exploiting the semantics of directed graphs. We also unearth the limitations of evaluations on directed graphs in previous works and propose a clear strategy for evaluating link prediction and graph reconstruction in directed graphs. We conduct extensive experiments to showcase our effectiveness on several real-world datasets on link prediction, node classification and graph reconstruction tasks. We show that the embeddings from our approach are indeed robust, generalizable and well performing across multiple kinds of tasks and graphs. We show that we consistently outperform all baselines for node classification task. In addition to providing a theoretical interpretation of our method we also show that we are considerably more robust than the other directed graph approaches.@en

Search engine-based features are a basic interaction mean for users to find information inside a Web-based Learning Management Systems (LMS); nonetheless, traditional solutions lack in mechanisms for access rights management for data contained in search engines' in- dexes. This paper explores the integration of a Role Based Access Control (RBAC) mechanism for the interaction with a search engine in a Web- based LMS. We first outline a reference conceptual model for the design of Web-based LMSs exploiting RBAC by means of WebML, a visual modeling language for the high-level specification of data-intensive Web applications. Then, we propose a model-driven approach for the definition of a RBAC-driven interaction between users and search engines, extending WebML with new modeling primitives and outlining significative modeling patterns for the specification of the visibility and action access control levels.@en