Publications on the Web can influence the public opinion about certain entities (e.g., politicians, institutions). At the same time, a variety of indicators can be extracted from these publications and used to estimate entity performance (e.g., popularity, votes share). This work proposes an automatic method that employs state-of-the-art natural language processing tools to extract indicators about entities mentioned in texts, for estimating the performance of these entities or semantically related ones. Our method calculates performance metrics from performance indicators consolidated for semantically related entities, assess correlations of these consolidated metrics with ground true performance, and uses these metrics to predict certain fluctuations in entity performance. Experimental results in a case study on politics show that consolidated metrics for several interrelated entities are better correlated to observed real performance measures of some target entities and lead to better predictions, than metrics for just one entity.

Mentions to real world things which are recognized by software tools in text often mismatch the ground truth. This paper proposes a formal classification of mention mismatching problems, including partial matching. Then, it depicts evidence that some longer mentions are associated with higher precision and more specific things than shorter mentions that overlap them. Based on this, some algorithms are proposed to automatically improve mentions by increasing their sizes whenever and as much as possible. Experimental results applying a variety of state-of-the-art annotation tools against several datasets made from real world texts show that over-segmentation (returned mention contained in the corresponding one of the ground truth) is the most prevalent partial matching problem among those of the proposed classification. In addition, some of the proposed algorithms for mention enhancing were able to correct most over-segmented mentions returned by tools used in the experiments with prominent benchmarks, leading to gains in precision and recall.

This paper presents an approach for Web service (WS) composition that enables the automatic adjustment of the composition at run-time to deal with WS execution flaws, e.g., WS unavailability, failure to execute properly or to produce desired results. The WS composition problem is converted into a nondeterministic planning problem which, by its turn, is mapped to SAT. Afterwards, a SAT solver obtains all the possible WS compositions with a maximum length N, which are then fused into a contingency strategy (CS) according to compatibility criteria. Such strategy enables the process execution engine to readily choose the most suitable WS composition to enact, and switch to an alternative composition with minimum adaptation cost in case of any WS execution flaw.

Despite recent progresses in methods for processing data about the movement of objects in the geographic space, some fundamental issues remain unresolved. One of them is how to describe movement segments (e.g., semantic trajectories, episodes like stops and moves) and diverse movement patterns (e.g., moving clusters, hotel-restaurant-shop-hotel), with formal semantic descriptions. Another issue is how to arrange descriptive data and measures in a Movement Data Warehouse (MDW) for powerful information analyses and reasonable performance. This paper introduces general definitions for movement segments, movement patterns, their categories and hierarchies. The proposed constructs are semantically enriched with references to concepts (categories) and/or instances of these concepts (objects) arranged in distinct hierarchies. Based on these constructs, we propose a semantic multidimensional model for MDW. A case study illustrates the expressiveness of the proposal for analyzing movement data collected via social media and semantically enriched with Linked Open Data (LOD).

This paper proposes an approach to efficientlyexecute conjunctive queries on big complex data together withtheir related conventional data. The basic idea is to horizontallyfragment the database according to criteria frequently usedin query predicates. The collection of fragments is indexed toefficiently find the fragment(s) whose contents satisfy some querypredicate(s). The contents of each fragment are then indexed aswell, to support efficient filtering of the fragment data according to other query predicate s) conjunctively connected to the former. This strategy has been applied to a collection of more than 106 million images together with their related conventional data. Experimental results show considerable performance gain of the proposed approach for queries with conventional and similaritybasedpredicates, compared to the use of a unique metric index for the entire database contents.