Like many other Specialised Information Services (FID), we are working with XSLT to map XML metadata from data providers to our data model, an extended version of the RDF-XML based Europeana Data Model (EDM). In the FID Performing Arts (FID DK), we currently receive data from 22 data providers that deliver 6 different official metadata standards like MARC21, EAD and LIDO as well as 10 individual data standards that result from working with database systems like MS Access or FAUST DB.

When hearing the term beacon, people might think of flares or a lighthouse at first. It can certainly be a guide in the ocean of different authority files on the web. In this post, I will take a closer look at BEACON files, their implementation and why they are a useful addition to discovery systems like the Specialised Information Service Performing Arts (FID DK). Introduction to BEACON Authority data disambiguates and represents controlled entities like persons, corporate bodies, places, topics, works and events via unique identifiers.

In the first post of this series, I looked at how to achieve parallel execution in Python using multiprocessing and discussed how this is unsuitable with WSGI-based web frameworks because WSGI only allows the web server to create new processes, not the framework. At the end, I mentioned several alternative Python HTTP servers which use asynchronous I/O with an event-loop-based scheduler to handle parallelism. In this post, we will look at how asynchronous I/O works in general, and specifically how it works in Python.

Python, popular though it is, has a few well-known weaknesses. One of the most well known among serious users of the language is the lack of multicore support for in-process threads. This is because CPython, Python’s standard implementation has a global interpreter lock (often referred to as the GIL). The GIL locks each instance of the interpreter to a single core—a common approach to avoid race conditions in the implementation of language interpreters.

The BIOfid Semantic Search Within the BIOfid-project, we create a semantic search portal (hereafter “BIOfid portal”) to help our users to access legacy biodiversity literature more easily. Hence, since the BIOfid portal has a deeper “understanding” of both the texts and the included species, it allows the users to get more relevant documents. Moreover, the BIOfid portal interprets the user query and transform it ad hoc into a graph database query, to learn more about their intention.