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Introduction Acute appendicitis is a common and serious situation during pregnancy, because of the increased risk of fetal loss and perforation in the third trimester, as well as a diagnostic difficulty. During recent years laparoscopic approach has been introduced to clinical practice with encouraging results. The purpose of this meta-analysis is to compare the surgical and obstetrical outcomes between laparoscopic and open appendectomy during pregnancy. Materials and methods MEDLINE, SCOPUS, Clinicaltrials.gov, CENTRAL and Google Scholar were searched for studies reporting on postoperative outcomes between laparoscopic and open appendectomy during pregnancy. The random effects model (DerSimonian–Laird) was used to calculate pooled effect estimates when high heterogeneity was encountered, otherwise the fixed-effects (Mantel–Haenszel) model was implemented. Results Twenty-one studies that enrolled 6276 pregnant women are included in the present meta-analysis. Of these women, 1963 underwent laparoscopic appendectomy and 4313 underwent an open appendectomy. Women who underwent laparoscopic appendectomy demonstrated an increase in fetal loss risk, while neonates of women that underwent open appendectomy presented decreased Apgar score at five minutes after birth. All the rest outcomes were similar between the two groups. The time that each study took place seemed to affect the comparison of birth weight and postoperative hospital stay between the two groups. Conclusion Laparoscopic appendectomy seems to be a relatively safe therapeutic option in pregnancy when it is indicated. Thus, it should be implemented in clinical practice, always considering the experience of the surgeon in such procedures. Nevertheless, the need of new studies to enhance this statement remains crucial.

Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens. OBDA approach has a great potential to facilitate such tasks; however, it has a number of limitations in dealing with analytics that restrict its use in important industrial applications. Based on our experience with Siemens, we argue that in order to overcome those limitations OBDA should be extended and become analytics, source, and cost aware. In this work we propose such an extension. In particular, we propose an ontology, mapping, and query language for OBDA, where aggregate and other analytical functions are first class citizens. Moreover, we develop query optimisation techniques that allow to efficiently process analytical tasks over static and streaming data. We implement our approach in a system and evaluate our system with Siemens turbine data.This work was partially funded by the EU project Optique (FP7-ICT-318338) and the EPSRC projects MaSI 3 , DBOnto, and ED 3 arXiv:1607.05351v2 [cs.AI]

Ontology-based data access (OBDA) is a popular approach for integrating and querying multiple data sources by means of a shared ontology. The ontology is linked to the sources using mappings, which assign views over the data to ontology predicates. Motivated by the need for OBDA systems supporting database-style aggregate queries, we propose a bag semantics for OBDA, where duplicate tuples in the views defined by the mappings are retained, as is the case in standard databases. We show that bag semantics makes conjunctive query answering in OBDA CONP-hard in data complexity. To regain tractability, we consider a rather general class of queries and show its rewritability to a generalisation of the relational calculus to bags.