Objective – To describe the rationale for and development of MetriDoc, an information technology infrastructure that facilitates the collection, transport, and use of library activity data.
Methods – With the help of the Institute for Museum and Library Services, the University of Pennsylvania Libraries have been working on creating a decision support system for library activity data. MetriDoc is a means of “lighting up” an array of data sources to build a comprehensive repository of quantitative information about services and user behavior. A data source can be a database, text file, Extensible Markup Language (XML), or any binary object that contains data and has business value. MetriDoc provides simple tools to extract useful information from various data sources; transform, resolve, and consolidate that data; and finally store them in a repository.
Results – The Penn Libraries completed five reference projects to prove basic concepts of the MetriDoc framework and make available a set of applications that other institutions could test in a deployment of the MetriDoc core. These reference projects are written as configurable plugins to the core framework and can be used to parse and store EZ-Proxy log data, COUNTER data, interlibrary loan transactional data from ILLIAD, fund expenditure data from the Voyager integrated library system, and transactional data from the Relais platform, which supports the BorrowDirect and EZBorrow resource sharing consortiums. The MetriDoc framework is currently undergoing test implementations at the University of Chicago and North Carolina State University, and the Kuali-OLE project is actively considering it as the basis of an analytics module.
Conclusion – If libraries decide that a business intelligence infrastructure is strategically important, deep collaboration will be essential to progress, given the costs and complexity of the challenge.
High-Precision Binary Object Detector Based on a BSF-XNOR Convolutional Layer
