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In this paper, we consider the problem of answering queries using materialized views in the presence of negative goals. The solution is carried out by 'inverting' views and deriving both positive and negative knowledge. In order to derive negative knowledge, we invert conjunctive views with negation into a set of (extended) views which may also have, in addition to negation-as-failure, a different form of negation called classical (or strong) negation. We also consider the case of disjunctive views and present a technique which permits us to infer both positive and negative knowledge. Furthermore, we extend previous techniques for inferring knowledge from views based on relations with functional dependencies. Finally, we present a prototype of a system developed at the University of Calabria. Index Terms--Relational databases, queries, views, information integration.

Abstract - This paper describes the design and a prototypical implementation of COMPLEX, a logic-based system extended with concepts from the object-oriented paradigm, intended as a tool for the development of knowledge-based applications. The system supports a logic language, called Complex-Datalog (C-Datalog), enhanced by semantic constructs to provide facility for data abstraction. Its implementation is based on a bottom-up computational model that guarantees a fully declarative style of programming. However, the user is also given the possibility of running a query using a top-down model of computation. Efficiency of execution is the result of the integration of different novel technologies for the compilation and the execution of queries.

Abstract Background Isotope-coded affinity tags (ICAT) is a method for quantitative proteomics based on differential isotopic labeling, sample digestion and mass spectrometry (MS). The method allows the identification and relative quantification of proteins present in two samples and consists of the following phases. First, cysteine residues are either labeled using the ICAT Light or ICAT Heavy reagent (having identical chemical properties but different masses). Then, after whole sample digestion, the labeled peptides are captured selectively using the biotin tag contained in both ICAT reagents. Finally, the simplified peptide mixture is analyzed by nanoscale liquid chromatography-tandem mass spectrometry (LC-MS/MS). Nevertheless, the ICAT LC-MS/MS method still suffers from insufficient sample-to-sample reproducibility on peptide identification. In particular, the number and the type of peptides identified in different experiments can vary considerably and, thus, the statistical (comparative) analysis of sample sets is very challenging. Low information overlap at the peptide and, consequently, at the protein level, is very detrimental in situations where the number of samples to be analyzed is high. Results We designed a method for improving the data processing and peptide identification in sample sets subjected to ICAT labeling and LC-MS/MS analysis, based on cross validating MS/MS results. Such a method has been implemented in a tool, called EIPeptiDi, which boosts the ICAT data analysis software improving peptide identification throughout the input data set. Heavy/Light (H/L) pairs quantified but not identified by the MS/MS routine, are assigned to peptide sequences identified in other samples, by using similarity criteria based on chromatographic retention time and Heavy/Light mass attributes. EIPeptiDi significantly improves the number of identified peptides per sample, proving that the proposed method has a considerable impact on the protein identification process and, consequently, on the amount of potentially critical information in clinical studies. The EIPeptiDi tool is available at http://bioingegneria.unicz.it/~veltri/projects/eipeptidi/ with a demo data set. Conclusion EIPeptiDi significantly increases the number of peptides identified and quantified in analyzed samples, thus reducing the number of unassigned H/L pairs and allowing a better comparative analysis of sample data sets.