Abstract
We study the predictive ability of some association rule measures typically used to assess descriptive interest. Such measures, namely conviction, lift and chi(2) are compared with confidence, Laplace, mutual information, cosine, Jaccard and phi-coefficient. As prediction models, we use sets of association rules. Classification is done by selecting the best rule, or by weighted voting. We performed an evaluation on 17 datasets with different characteristics and conclude that conviction is on average the best predictive measure to use in this setting. We also provide some meta-analysis insights for explaining the results.

Abstract
Various programming languages allow the construction of structure-shy programs. Such programs are defined generically for many different datatypes and only specify specific behavior for a few relevant subtypes. Typical examples are XML query languages that allow selection of subdocuments without exhaustively specifying intermediate element tags. Other examples are languages and libraries for polytypic or strategic functional programming and for adaptive object-oriented programming. In this paper, we present an algebraic approach to transformation of declarative structure-shy programs, in particular for strategic functions and XML queries. We formulate a rich set of algebraic laws, not just for transformation of structure-shy programs, but also for their conversion into structure-sensitive programs and vice versa. We show how subsets of these laws can be used to construct effective rewrite systems for specialization, generalization, and optimization of structure-shy programs. We present a type-safe encoding of these rewrite systems in Haskell which itself uses strategic functional programming techniques. Copyright © 2007 ACM.

Abstract
Coupled transformations occur in software evolution when multiple artifacts must be modified in such a way that they remain consistent with each other. An important example involves the coupled transformation of a data type, its instances, and the programs that consume or produce it. Previously, we have provided a formal treatment of transformation of the first two: data types and instances. The treatment involved the construction of type-safe, type-changing strategic rewrite systems. In this paper, we extend our treatment to the transformation of corresponding data processing programs. The key insight underlying the extension is that both data migration functions and data processors can be represented type-safely by a generalized abstract data type (GADT). These representations are then subjected to program calculation rules, harnessed in type-safe, type-preserving strategic rewrite systems. For ease of calculation, we use point-free representations and corresponding calculation rules. Thus, coupled transformations are carried out in two steps. First, a type-changing rewrite system is applied to a source type to obtain a target type together with (representations of) migration functions between source and target. Then, a type-preserving rewrite system is applied to the composition of a migration function and a data processor on the source (or target) type to obtain a data processor on the target (or source) type. All rewrites are type-safe.

Abstract
A two-level data transformation consists of a type-level transformation of a data format coupled with value level transformations of data instances corresponding to that format. We have implemented a system for performing two-level transformations on XML schemas and their corresponding documents, and on SQL schemas and the databases that they describe. The core of the system consists of a combinator library for composing type-changing rewrite rules that preserve structural information and referential constraints. We discuss the implementation of the system's core library, and of its SQL and XML front-ends in the functional language Haskell. We show how the system can be used to tackle various two-level transformation scenarios, such as XML schema evolution coupled with document migration, and hierarchical-relational data mappings that convert between XML documents and SQL databases.

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