Mirella M. Moro

Collaboration recommendation on academic social networks

In the academic context, scientific research works are often performed through collaboration and cooperation between researchers and research groups. Researchers work in various subjects and in several research areas. Identifying new partners to execute joint research and analyzing the level of cooperation of the current partners can be very complex tasks. Recommendation of new collaborations may be a valuable tool for reinforcing and discovering such partners. This paper presents an innovative approach to recommend collaborations on the context of academic Social Networks. Specifically, we introduce the architecture for such approach and the metrics involved in recommending collaborations. We also present an initial case study to validate our approach.

Preserving XML queries during schema evolution

In XML databases, new schema versions may be released as frequently as once every two weeks. This poster describes a taxonomy of changes for XML schema evolution. It examines the impact of those changes on schema validation and query evaluation. Based on that study, it proposes guidelines for XML schema evolution and for writing queries in such a way that they continue to operate as expected across evolving schemas.

Twig query processing over graph-structured XML data

XML and semi-structured data is usually modeled using graph structures. Structural summaries, which have been proposed to speedup XML query processing have graph forms as well. The existent approaches for evaluating queries over tree structured data (i.e. data whose underlying structure is a tree) are not directly applicable when the data is modeled as a random graph. Moreover, they cannot be applied when structural summaries are employed and, to the best of our knowledge, no analogous techniques have been reported for this case either. As a result, the potential of structural summaries is not fully exploited.In this paper, we investigate query evaluation techniques applicable to graph-structured data. We propose efficient algorithms for the case of directed acyclic graphs, which appear in many real world situations. We then tailor our approaches to handle other directed graphs as well. Our experimental evaluation reveals the advantages of our solutions over existing methods for graph-structured data.

Using link semantics to recommend collaborations in academic social networks

Social network analysis (SNA) has been explored in many contexts with different goals. Here, we use concepts from SNA for recommending collaborations in academic networks. Recent work shows that research groups with well connected academic networks tend to be more prolific. Hence, recommending collaborations is useful for increasing a groups connections, then boosting the group research as a collateral advantage. In this work, we propose two new metrics for recommending new collaborations or intensification of existing ones. Each metric considers a social principle (homophily and proximity) that is relevant within the academic context. The focus is to verify how these metrics influence in the resulting recommendations. We also propose new metrics for evaluating the recommendations based on social concepts (novelty, diversity and coverage) that have never been used for such a goal. Our experimental evaluation shows that considering our new metrics improves the quality of the recommendations when compared to the state-of-the-art.

RoXSum: Leveraging Data Aggregation and Batch Processing for XML Routing

Content-based routing is the primary form of communication within publish/subscribe systems. In those systems data transmission is performed by sophisticated overlay networks of content-based routers, which match data messages against registered subscriptions and forward them based on this matching. Despite their inherent complexities, such systems are expected to deliver information in a timely and scalable fashion. As a result, their successful deployment is a strenuous task. Relevant efforts have so far focused on the construction of the overlay network and the filtering of messages at each broker. However, the efficient transmission of messages has received less attention. In this work, we propose a solution that gracefully handles the transmission task, while providing performance benefits for the matching task as well. Along those lines, we design RoXSum, a message representation scheme that aggregates the routing information from multiple documents in a way that permits subscription matching directly on the aggregated content. Our performance study shows that RoXSum is a viable and effective technique, as it speeds up message routing for more than an order of magnitude.

Schema advisor for hybrid relational-XML DBMS

In response to the widespread use of the XML format for document representation and message exchange, major database vendors support XML in terms of persistence, querying and indexing. Specifically, the recently released IBM DB2 9 (for Linux, Unix and Windows) is a hybrid data server with optimized management of both XML and relational data. With the new option of storing and querying XML in a relational DBMS, data architects face the the decision of what portion of their data to persist as XML and what portion as relational data. This problem has not been addressed yet and represents a serious need in the industry. Hence, this paper describes ReXSA, a schema advisor tool that is being prototyped for IBM DB2 9. ReXSA proposes candidate database schemas given an information model of the enterprise data. It has the advantage of considering qualitative properties of the information model such as reuse, evolution and performance profiles for deciding how to persist the data. Finally, we show the viability and practicality of ReXSA by applying it to custom and real usecases.

TVQL - Temporal Versioned Query Language

The Temporal Versions Model (TVM) is an Object Oriented Data Model developed to store the object versions and, for each version, the history of its dynamic attributes and relationships values. In this work, we propose a query language for this model. The language, called Temporal Versioned Query Language - TVQL, is based on SQL, adding new features to recover temporal information and versions. An alternative to its implementation on top of a commercial database is also presented.

Sentiment-based influence detection on Twitter

The user generated content available in online communities is easy to create and consume. Lately, it also became strategically important to companies interested in obtaining population feedback on products, merchandising, etc. One of the most important online communities is Twitter: recent statistics report 65 million new tweets each day. However, processing this amount of data is very costly and a big portion of the content is simply not useful for strategic analysis. Thus, in order to filter the data to be analyzed, we propose a new method for ranking the most influential users in Twitter. Our approach is based on a combination of the user position in networks that emerge from Twitter relations, the polarity of her opinions and the textual quality of her tweets. Our experimental evaluation shows that our approach can successfully identify some of the most influential users and that interactions between users provide the best evidence to determine user influence.

Adding Time to an Object-Oriented Versions Model

In this paper, we propose an object-oriented version model which presents temporal concepts to store not only the object lifetime but also the history of dynamic attributes and relationships defined in the versioned objects and versions. One of the main features of our model is the possibility of having two different time orders, branched time for the object and linear time for each version. The model supports integration with existing databases, by allowing the modeling of normal classes among the temporal versioned classes. Finally, an approach to its implementation on top of a commercial database is presented.

A temporal database management system implemented on top of a conventional database

Temporal data models have proven to be convenient to specify applications, allowing the representation of the temporal evolution of data. Several temporal data models were proposed in the last 20 years with this purpose, but no commercial implementation of a temporal database is still available. This paper presents an integrated temporal database environment implemented on top of a conventional database. Using this environment, a user can handle the specification, the data definition and the queries as though the database implements the temporal data model. The environment performs the mapping from the temporal conceptual schema to the correspondent database, and of the queries expressed in the temporal query language to SQL. Data definition is controlled based on the state transition rules of the temporal data model, keeping thus the temporal integrity of the database. The underlying conventional database keeps transparent to the user.