A Model for Compound Type Changes Encountered in Schema Evolution

Schema evolution is a problem that is faced by long-lived data. When a schema changes, existing persistent data can become inaccessible unless the database system provides mechanisms to access data created with previous versions of the schema. Most existing systems that support schema evolution focus on changes local to individual types within the schema, thereby limiting the changes that the database maintainer can perform. We have developed a model of type changes incorporating changes local to individual types as well as compound changes involving multiple types. The model describes both type changes and their impact on data by defining derivation rules to initialize new data based on the existing data. The derivation rules can describe local and nonlocal changes to types to capture the intent of a large class of type change operations. We have built a system called Tess (Type Evolution Software System) that uses this model to recognize type changes by comparing schemas and then produces a transformer that can update data in a database to correspond to a newer version of the schema

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

Recent genomic analyses of pathologically-defined tumor types identify “within-a-tissue” disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head & neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multi-platform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All datasets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies

Type Evolution Support for Complex Type Changes

Type evolution is a serious problem for programs that use persistent data. Type changes are required during software maintenance, but can make persistent data inaccessible. Existing systems that support type evolution severely restrict the ways in which a type can be changed, thereby limiting what can be accomplished during maintenance. Tess is a system to automate type evolution for a collection of more complex type changes while providing a more natural editing environment for the programmer. Tess uses a comparative approach to identify type changes rather than a specialized editing process. Experimental results on real data indicate that Tess can accurately identify more complex type changes than those supported by existing systems relying on specialized editing. 1 Motivation Software maintenance is a serious problem for organizations developing and using software. The maintenance activity is widely recognized as extremely difficult and error-prone, with costs typically exceeding t..

Model Checking of Software Processes

Software process and workflow languages are increasingly used to define loosely-coupled systems of systems. These languages focus on coordination issues such as data flow and control flow among the subsystems and exception handling activities. The resulting systems are often highly concurrent with activities distributed over many computers. Adequately testing these systems is not feasible due to their size, concurrency, and distributed implementation. Furthermore, the concurrent nature of their activities makes it likely that errors related to the order in which activities are interleaved will go undetected during testing. As a result, verification using static analysis seems necessary to increase confidence in the correctness of these systems