Abstract Natural language processing techniques can be used to analyze the linguistic content of a document to extract missing pieces of metadata. However, accurate metadata extraction may not depend solely on the linguistics, but also on structural problems such as extremely large documents, unordered multi‐file documents, and inconsistency in manually labeled metadata. In this work, we start from two standard machine learning solutions to extract pieces of metadata from Environmental Impact Statements, environmental policy documents that are regularly produced under the US National Environmental Policy Act of 1969. We present a series of experiments where we evaluate how these standard approaches are affected by different issues derived from real‐world data. We find that metadata extraction can be strongly influenced by nonlinguistic factors such as document length and volume ordering and that the standard machine learning solutions often do not scale well to long documents. We demonstrate how such solutions can be better adapted to these scenarios, and conclude with suggestions for other NLP practitioners cataloging large document collections.

The popularity of distributed computing platforms (e.g., Hadoop) is largely due to their ability to address scalability issues that arise from data storage and processing limitations of standard computing systems. However, the decision to dedicate organizational resources and capital for such systems needs a careful consideration of several factors including evaluation of cloud-based distributed computing options. We propose a framework of metrics which we used to conduct an in-depth performance and cost benefit analysis of two standard Hadoop infrastructural choices, i.e., a Platform as a Service (PaaS) on-demand cloud setup and a local organizational setup. We evaluated the framework by means of an exploratory data analysis use-case for a large-scale graph processing research problem. Our analysis considered highly granular aspects of distributed computing performance and studied how utilization rates and infrastructure amortization times affect break-even times. We identified that virtual memory management adversely affects the performance of a cloud cluster during the reduce phase with the magnitude of degradation dependent on the type of MapReduce operation. Our study is intended not only as an evaluation of infrastructural choices but also a development of a metric framework that can serve as a baseline for researchers examining distributed infrastructures.

Data mining models for high-cost patient encounter prediction at the point-of-admission (HPEPP) in inpatient wards are scarce in the literature. This is due to the lack of availability of relevant features at such an early stage of treatment. In this study, we create a disease co-occurrence network (DCN) using a subset of the state inpatient database of Arizona. We explore this network for community formation and structural properties to create new input features for HPEPP models. Tree-based data mining models are trained using input feature sets including these new network features, and distinct disease communities in the DCN are identified. We propose community membership and high-cost propensity scores as two network-based features for HPEPP modeling. We compare the performance of models with different input feature sets and find that the new features significantly improve the accuracy sensitivity and specificity of prediction models. This model has the potential to improve targeted care management and reduce health care expenditure.

If past versions of XML documents are retained, what of the various integrity constraints defined in XML Schema on those documents? This paper describes how to interpret such constraints as sequenced constraints, applicable at each point in time. We also consider how to add new variants that apply across time, so-called nonsequenced constraints. Our approach supports temporal documents that vary over both valid and transaction time, whose schema can vary over transaction time. We do this by replacing the schema with a (possibly time-varying) temporal schema and replacing the document with a temporal document, both of which are upward compatible with conventional XML and with conventional tools like XMLLINT, which we have extended to support the temporal constraints introduced here.