Graph integration of structured, semistructured and unstructured data for data journalism

Abstract

Nowadays, journalism is facilitated by the existence of large amounts of digital data sources, including many Open Data ones. Such data sources are extremely heterogeneous, ranging from highly structured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists (and other classes of users lacking advanced IT expertise, such as most non-governmentalorganizations, or small public administrations) need to be able to make sense of such heterogeneous corpora, even if they lack the ability to de ne and deploy custom extract-transform-load work ows. These are di cult to set up not only for arbitrary heterogeneous inputs, but also given that users may want to add (or remove) datasets to (from) the corpus.We describe a complete approach for integrating dynamic sets of heterogeneous data sources along the lines described above: the challenges we faced to make such graphs useful, allow their integration to scale, and the solutions we proposed for these problems. Our approach is implemented within the ConnectionLens system; we validate it through a set of experiments.

Integration between Moodle and Academic Information System using Restful API for Online Learning

“…In this research, the method used is the synchronization method [22] by combining heterogeneous data [23], namely using a way of linking data between AIS and LMS through the application programming interface (API) on the two systems. An integration semi structured and unstructured data [24] system will be built that can accommodate data from AIS and LMS, the integration system will have the ability to control the process of creating read update delete courses without having to open the LMS.…”

Discovering Conflicts of Interest across Heterogeneous Data Sources with ConnectionLens

“…On this graph, the GAM keyword search algorithm, described in Section 3.2, answers queries such as the one required for our CoI scenario. Technical details as well as experimental validations of choices made in our system are included in [3].…”

“…To keep the graph compact, one of the 𝑝 nodes is declared the representative of all 𝑝 nodes, and instead, we only store the 𝑝 − 1 equivalence edges adjacent to the representative. Details on all the above graph construction steps can be found in [3].…”

“…For instance, an expert and a company may both have "nationality" edges leading to "French" (a solution of 2 edges), but that may be less interesting than finding that the expert has written an article specifying in its CoIStatement funding from the company (which could span over five edges or more). Our Grow-and-Aggressive-Merge (GAM) algorithm [3,5] enumerates trees exhaustively, until a number of answers are found, or a time-out. First, it builds 1-node trees from the nodes of 𝐺 which match 1 or more keywords, e.g., 𝑡 1 , 𝑡 2 , 𝑡 3 in Figure 3, showing some partial trees built when answering our sample query.…”

“…Importantly, GAM can be used with any score function. Its details are described in [3,5] and its scalability with different graph models and sizes is demostrated in [4].…”