42,190 research outputs found
A Rule-Based Approach to Analyzing Database Schema Objects with Datalog
Database schema elements such as tables, views, triggers and functions are
typically defined with many interrelationships. In order to support database
users in understanding a given schema, a rule-based approach for analyzing the
respective dependencies is proposed using Datalog expressions. We show that
many interesting properties of schema elements can be systematically determined
this way. The expressiveness of the proposed analysis is exemplarily shown with
the problem of computing induced functional dependencies for derived relations.
The propagation of functional dependencies plays an important role in data
integration and query optimization but represents an undecidable problem in
general. And yet, our rule-based analysis covers all relational operators as
well as linear recursive expressions in a systematic way showing the depth of
analysis possible by our proposal. The analysis of functional dependencies is
well-integrated in a uniform approach to analyzing dependencies between schema
elements in general.Comment: Pre-proceedings paper presented at the 27th International Symposium
on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur,
Belgium, 10-12 October 2017 (arXiv:1708.07854
Ensuring Query Compatibility with Evolving XML Schemas
During the life cycle of an XML application, both schemas and queries may
change from one version to another. Schema evolutions may affect query results
and potentially the validity of produced data. Nowadays, a challenge is to
assess and accommodate the impact of theses changes in rapidly evolving XML
applications.
This article proposes a logical framework and tool for verifying
forward/backward compatibility issues involving schemas and queries. First, it
allows analyzing relations between schemas. Second, it allows XML designers to
identify queries that must be reformulated in order to produce the expected
results across successive schema versions. Third, it allows examining more
precisely the impact of schema changes over queries, therefore facilitating
their reformulation
A methodology for collecting valid software engineering data
An effective data collection method for evaluating software development methodologies and for studying the software development process is described. The method uses goal-directed data collection to evaluate methodologies with respect to the claims made for them. Such claims are used as a basis for defining the goals of the data collection, establishing a list of questions of interest to be answered by data analysis, defining a set of data categorization schemes, and designing a data collection form. The data to be collected are based on the changes made to the software during development, and are obtained when the changes are made. To insure accuracy of the data, validation is performed concurrently with software development and data collection. Validation is based on interviews with those people supplying the data. Results from using the methodology show that data validation is a necessary part of change data collection. Without it, as much as 50% of the data may be erroneous. Feasibility of the data collection methodology was demonstrated by applying it to five different projects in two different environments. The application showed that the methodology was both feasible and useful
Keeping the Cost of Process Change Low through Refactoring
With the increasing adoption of process-aware information systems (PAIS) large process model repositories have emerged. Over time respective models have to be re-aligned to the real world business processes through customization or adaptation. This bears the risk that model redundancies are introduced and complexity is increased. If no continuous investment is made in keeping models simple, changes are becoming increasingly costly and error-prone. Although refactoring techniques are widely used in software engineering to address related problems, this does not yet constitute state-of-the art in business process management. Consequently, process designers either have to refactor process models by hand or can not apply respective techniques at all. In this paper we propose a set of techniques for refactoring large process repositories, which are behaviour-preserving. The proposed refactorings enable process designers to effectively deal with model complexity by making process models easier to change, less error-prone and better understandable
Curriculum Guidelines for Undergraduate Programs in Data Science
The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program
met for the purpose of composing guidelines for undergraduate programs in Data
Science. The group consisted of 25 undergraduate faculty from a variety of
institutions in the U.S., primarily from the disciplines of mathematics,
statistics and computer science. These guidelines are meant to provide some
structure for institutions planning for or revising a major in Data Science
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