Summary. We propose a new 'fast subset scan' approach for accurate and computationally efficient event detection in massive data sets. We treat event detection as a search over subsets of data records, finding the subset which maximizes some score function. We prove that many commonly used functions (e.g. Kulldorff's spatial scan statistic and extensions) satisfy the 'linear time subset scanning' property, enabling exact and efficient optimization over subsets. In the spatial setting, we demonstrate that proximity-constrained subset scans substantially improve the timeliness and accuracy of event detection, detecting emerging outbreaks of disease 2 days faster than existing methods.

Event detection in social media is an important but challenging problem. Most existing approaches are based on burst detection, topic modeling, or clustering techniques, which cannot naturally model the implicit heterogeneous network structure in social media. As a result, only limited information, such as terms and geographic locations, can be used. This paper presents Non-Parametric Heterogeneous Graph Scan (NPHGS), a new approach that considers the entire heterogeneous network for event detection: we first model the network as a "sensor" network, in which each node senses its "neighborhood environment" and reports an empirical pvalue measuring its current level of anomalousness for each time interval (e.g., hour or day). Then, we efficiently maximize a nonparametric scan statistic over connected subgraphs to identify the most anomalous network clusters. Finally, the event represented by each cluster is summarized with information such as type of event, geographical locations, time, and participants. As a case study, we consider two applications using Twitter data, civil unrest event detection and rare disease outbreak detection, and present empirical evaluations illustrating the effectiveness and efficiency of our proposed approach.

Mucous membrane pemphigoid (MMP) is an autoimmune blistering disease frequently associated with scarring of involved clinical sites. At present, therapeutic intervention in the form of immunomodulating or immunosuppressive agents is often reserved until the onset of significant inflammation and/or early cicatrization. We have therefore studied the clinical and immunopathological findings in 67 patients with MMP in order to try to establish a reliable prognostic indicator by which patients at high risk may be identified early in the disease. Inclusion criteria were a predominantly mucosal disease and the detection of IgG and/or C3 anti-basement membrane zone (BMZ) immunoreactants using immunofluorescence techniques. Patients were allocated to three disease subgroups on the basis of the modality and duration of therapeutic intervention required to achieve effective control of disease. In addition, at presentation and at each follow-up visit, a clinical score for severity of involved clinical sites was awarded and serum collected for indirect immunofluorescence (IIF). A dual circulating anti-basement membrane zone (anti-BMZ) antibody response with IgG and IgA was significantly associated with a more severe and persistent disease profile (P < 0.001). The odds ratios for requiring systemic therapy were: 11.6 among patients in whom there was a clinical score > or = 5 compared with a score < 5, and 31.3 and 66.9 among patients with IgG alone and both IgG and IgA, respectively, compared with negative IIF. The findings suggest that an assessment based upon a combination of site severity score and the presence of circulating IgG and IgA by IIF using 1 mol/L salt-split human skin substrate may be considered a useful prognostic indicator.

School shootings tear the fabric of society. In the wake of a school shooting, parents, pediatricians, policymakers, politicians, and the public search for "the" cause of the shooting. But there is no single cause. The causes of school shootings are extremely complex. After the Sandy Hook Elementary School rampage shooting in Newtown, Connecticut, we wrote a report for the National Science Foundation on what is known and not known about youth violence. This article summarizes and updates that report. After distinguishing violent behavior from aggressive behavior, we describe the prevalence of gun violence in the United States and age-related risks for violence. We delineate important differences between violence in the context of rare rampage school shootings, and much more common urban street violence. Acts of violence are influenced by multiple factors, often acting together. We summarize evidence on some major risk factors and protective factors for youth violence, highlighting individual and contextual factors, which often interact. We consider new quantitative "data mining" procedures that can be used to predict youth violence perpetrated by groups and individuals, recognizing critical issues of privacy and ethical concerns that arise in the prediction of violence. We also discuss implications of the current evidence for reducing youth violence, and we offer suggestions for future research. We conclude by arguing that the prevention of youth violence should be a national priority.

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