Aggregate factors (that is, those based on aggregate functions such as SUM, AVERAGE, AND etc.) in probabilistic relational models can compactly represent dependencies among a large number of relational random variables. However, propositional inference on a factor aggregating n k-valued random variables into an r-valued result random variable is O(r k 2n).
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder that has been linked to toxic aggregates of poly-Gly-Ala (poly-GA) peptides generated by aberrant translation of an expanded nucleotide repeat sequence. Proteasomes are cytosolic molecular machines involved in the degradation of misfolded and aggregated proteins. Guo et al. used cryo–electron tomography to examine the molecular architecture of poly-GA aggregates in situ in intact neurons. The peptide aggregates formed twisted ribbons that clumped together and that were surrounded by proteasomes trapped in their normally transient substrate-processing conformation. The extent of proteasome accumulation was such that the ability of the remaining proteasomes within the neuron to perform their normal housekeeping functions was likely to be impaired, potentially explaining the neuronal pathologies observed in ALS.
We propose an algorithm to reformulate aggregate queries using views in a data integration LAV setting. Our algorithm considers a special case of reformulations where aggregates in the query are expressed as views over aggregates in the view definitions. Although the problem of determining whether two queries are equivalent is undecidable, our algorithm returns an equivalent rewriting if one exists.
In this paper we describe our approach for representing temporal aggregates in OWL, as an extension to the initial version of the OWL-Time, a temporal ontology for describing the temporal content of Web pages and the temporal properties of Web services. We represent the temporal aggregates ontology in both first-order logic axioms and OWL encodings. We also present several examples in detail to show how our ontology can be used to represent complex multiple-layered and conditional temporal aggregates for the Semantic Web.