We present a comprehensive system for extracting metadata from scholarly articles. In our approach the entire document is inspected, including headers and footers of all the pages as well as bibliographic references. The system is based on a modular workflow which allows for evaluation, unit testing and replacement of individual components. The workflow is optimized towards processing of born-digital documents, but may accept scanned document images as well. The machinelearning approaches we have chosen for solving individual tasks increase the ability to adapt to new document layouts and formats. The evaluation tests we have performed showed good results of the individual implementations and the entire metadata extraction process.

No abstract

Abstract. At CeON ICM UW we are in possession of a large collection of scholarly documents that we store and process using MapReduce paradigm. One of the main challenges is to design a simple, but effective data model that fits various data access patterns and allows us to perform diverse analysis efficiently. In this paper, we will describe the organization of our data and explain how this data is accessed and processed by open-source tools from Apache Hadoop Ecosystem.

Content Analysis System (CoAnSys) is a research framework for mining scientific publications using Apache Hadoop. This article describes the algorithms currently implemented in CoAnSys including classification, categorization and citation matching of scientific publications. The size of the input data classifies these algorithms in the range of big data problems, which can be efficiently solved on Hadoop clusters.

No abstract