Securing Provenance

Provenance describes how an object came to be in its present state. Intelligence dossiers, medical records and corporate financial reports capture provenance information. Many of these applications call for security, but existing security models are not up to the task. Provenance is a causality graph with annotations. The causality graph connects the various participating objects describing the process that produced an object’s present state. Each node represents an object and each edge represents a relationship between two objects. This graph is an immutable directed acyclic graph (DAG). Existing security models do not apply to DAGs nor do they easily extend to DAGs. Any model to control access to the structure of the graph must integrate with existing security models for the objects. We need to develop an access control model tailored to provenance and study how it interacts with existing access control models. This paper frames the problem and identifies issues requiring further research.Engineering and Applied Science

Provenance-Aware Sensor Data Storage

Sensor network data has both historical and realtime value. Making historical sensor data useful, in particular, requires storage, naming, and indexing. Sensor data presents new challenges in these areas. Such data is location-specific but also distributed; it is collected in a particular physical location and may be most useful there, but it has additional value when combined with other sensor data collections in a larger distributed system. Thus, arranging location-sensitive peer-to-peer storage is one challenge. Sensor data sets do not have obvious names, so naming them in a globally useful fashion is another challenge. The last challenge arises from the need to index these sensor data sets to make them searchable. The key to sensor data identity is provenance, the full history or lineage of the data. We show how provenance addresses the naming and indexing issues and then present a research agenda for constructing distributed, indexed repositories of sensor data.Engineering and Applied Science

Provenance-Aware Sensor Data Storage

Sensor network data has both historical and realtime value. Making historical sensor data useful, in particular, requires storage, naming, and indexing. Sensor data presents new challenges in these areas. Such data is location-specific but also distributed; it is collected in a particular physical location and may be most useful there, but it has additional value when combined with other sensor data collections in a larger distributed system. Thus, arranging location-sensitive peer-to-peer storage is one challenge. Sensor data sets do not have obvious names, so naming them in a globally useful fashion is another challenge. The last challenge arises from the need to index these sensor data sets to make them searchable. The key to sensor data identity is provenance, the full history or lineage of the data. We show how provenance addresses the naming and indexing issues and then present a research agenda for constructing distributed, indexed repositories of sensor data.Engineering and Applied Science

Choosing a Data Model and Query Language for Provenance

The ancestry relationships found in provenance form a directed graph. Many provenance queries require traversal of this graph. The data and query models for provenance should directly and naturally address this graph-centric nature of provenance. To that end, we set out the requirements for a provenance data and query model and discuss why the common solutions (relational, XML, RDF) fall short. A semistructured data model is more suited for handling provenance. We propose a query model based on the Lorel query language, and briefly describe how our query language PQL extends Lorel.Engineering and Applied Science

Provenance Integration Requires Reconciliation

While there has been a great deal of research on provenance systems, there has been little discussion about challenges that arise when making different provenance systems interoperate. In fact, most of the literature focuses on provenance systems in isolation and does not discuss interoperability – what it means, its requirements, and how to achieve it. We designed the Provenance-Aware Storage System to be a general- purpose substrate on top of which it would be “easy” to add other provenance-aware systems in a way that would provide “seamless integration” for the provenance captured at each level. While the system did exactly what we wanted on toy problems, when we began integrating StarFlow, a Python-based workflow/provenance system, we discovered that integration is far trickier and more subtle than anyone has suggested in the literature. This work describes our experience undertaking the integration of StarFlow and PASS, identifying several important additions to existing provenance models necessary for interoperability among provenance systems.Engineering and Applied Science

Layering in Provenance Systems

Digital provenance describes the ancestry or history of a digital object. Most existing provenance systems, however, operate at only one level of abstraction: the sys- tem call layer, a workflow specification, or the high-level constructs of a particular application. The provenance collectable in each of these layers is different, and all of it can be important. Single-layer systems fail to account for the different levels of abstraction at which users need to reason about their data and processes. These systems cannot integrate data provenance across layers and cannot answer questions that require an integrated view of the provenance. We have designed a provenance collection structure facilitating the integration of provenance across multiple levels of abstraction, including a workflow engine, a web browser, and an initial runtime Python provenance tracking wrapper. We layer these components atop provenance-aware network storage (NFS) that builds upon a Provenance-Aware Storage System (PASS). We discuss the challenges of building systems that integrate provenance across multiple layers of abstraction, present how we augmented systems in each layer to integrate provenance, and present use cases that demonstrate how provenance spanning multiple layers provides functionality not available in existing systems. Our evaluation shows that the overheads imposed by layering provenance systems are reasonable.Engineering and Applied Science

Calcium antagonists and mortality in patients with coronary artery disease: A Cohort study of 11,575 patients

AbstractObjectives. This study sought to establish the risk ratio for mortality associated with calcium antagonists in a large population of patients with chronic coronary artery disease.Background. Recent reports have suggested that the use of short-acting nifedipine may cause an increase in overall mortality in patients with coronary artery disease and that a similar effect may be produced by other calcium antagonists, in particular those of the dihydropyridine type.Methods. Mortality data were obtained for 11,575 patients screened for the Bezafibrate Infarction Prevention study (5,843 with and 5,732 without calcium antagonists) after a mean follow-up period of 3.2 years.Results. There were 495 deaths (8.5%) in the calcium antagonist group compared with 410 in the control group (7.2%). The age-adjusted risk ratio for mortality was 1.08 (95% confidence interval [CI] 0.95 to 1.24). After adjustment for the differences between the groups in age and gender and the prevalence of previous myocardial infarction, angina pectoris, hypertension, New York Heart Association functional class, peripheral vascular disease, chronic obstructive pulmonary disease, diabetes and current smoking, the adjusted risk ratio declined to 0.97 (95% CI 0.84 to 1.11). After further adjustment for concomintant medication, the risk ratio was estimated at 0.94 (95% CI 0.82 to 1.08).Conclusions. The current analysis does not support the claim that calcium antagonist therapy in patients with chronic coronary artery disease, whether myocardial infarction survivors or others, harbors an increased risk of mortality

Layering in Provenance-Aware Storage Systems

Digital provenance describes the ancestry or history of a digital document. Provenance provides answers to questions such as: “How does the ancestry of these objects differ?” “Are there source code files tainted by proprietary software?” “How was this object created?” Prior systems used to collect and maintain provenance operate within a single layer of abstraction: the system call boundary, a workflow specification language, or in a domain-specific application level. The provenance collected at each of these layers of abstraction is different, and all of it is important at one time or another. All of these solutions fundamentally fail to account for the different layers of abstraction at which users need to reason about their data and processes. None of these systems support queries across different layers of abstraction to answer a question such as “The calculated values in my spreadsheet have changed. Is this due to a change in the spreadsheet, a difference in the spreadsheet application, the libraries being used, or the operating system being used?” We present an architecture for provenance collection that facilitates the integration of provenance across multiple layers of abstraction and across network boundaries. We show how the need to support provenance collection at multiple layers drives the architecture. We present provenance-aware use cases from the field of thermography and quantify system overheads, showing that we can provide new functionality with acceptable overhead.Engineering and Applied Science

Crustal structure of central Lake Baikal : insights into intracontinental rifting

This paper is not subject to U.S. copyright. The definitive version was published in Journal of Geophysical Research 107, B7 (2002): 2132, doi:10.1029/2001JB000300.The Cenozoic rift system of Baikal, located in the interior of the largest continental mass on Earth, is thought to represent a potential analog of the early stage of breakup of supercontinents. We present a detailed P wave velocity structure of the crust and sediments beneath the Central Basin, the deepest basin in the Baikal rift system. The structure is characterized by a Moho depth of 39–42.5 km; an 8-km-thick, laterally continuous high-velocity (7.05–7.4 km/s) lower crust, normal upper mantle velocity (8 km/s), a sedimentary section reaching maximum depths of 9 km, and a gradual increase of sediment velocity with depth. We interpret the high-velocity lower crust to be part of the Siberian Platform that was not thinned or altered significantly during rifting. In comparison to published results from the Siberian Platform, Moho under the basin is elevated by <3 km. On the basis of these results we propose that the basin was formed by upper crustal extension, possibly reactivating structures in an ancient fold-and-thrust belt. The extent and location of upper mantle extension are not revealed by our data, and it may be offset from the rift. We believe that the Baikal rift structure is similar in many respects to the Mesozoic Atlantic rift system, the precursor to the formation of the North Atlantic Ocean. We also propose that the Central Baikal rift evolved by episodic fault propagation and basin enlargement, rather than by two-stage rift evolution as is commonly assumed.This project was jointly funded by the U.S. Geological Survey Coastal and Marine Program and the Russian Academy of Sciences

International Consensus Statement on Rhinology and Allergy: Rhinosinusitis

Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR‐RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR‐RS‐2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence‐based findings of the document. Methods: ICAR‐RS presents over 180 topics in the forms of evidence‐based reviews with recommendations (EBRRs), evidence‐based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICAR‐RS‐2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence‐based management algorithm is provided. Conclusion: This ICAR‐RS‐2021 executive summary provides a compilation of the evidence‐based recommendations for medical and surgical treatment of the most common forms of RS