Pablo Castro

ADO.NET entity framework: raising the level of abstraction in data programming

The ADO.NET Entity Framework provides a persistence layer for .NET applications that allows developers to work at a higher level of abstraction when interacting with data and data-access interfaces. Developers can model and access their data using a conceptual schema that is mapped to a relational database via a flexible mapping. Interaction with the data can take place using a SQL-based data manipulation language and iterator APIs, or through an object-based domain model in the spirit of object-to-relational mappers.We demonstrate how the Entity Framework simplifies application development using sample scenarios. We illustrate how the data is modeled, queried and presented to the developer. We also show how the provided data programming infrastructure can result in easier-to-understand code by making its intent more explicit, as well as how it can help with maintenance by adding a level of indirection between the logical database schema and the conceptual model that applications operate on.

Astoria: A Programming Model for Data on the Web

Modern Web applications built using technologies such as AJAX, Adobe flash and Microsoft Silverlight interact with data in a different way compared to previous-generation applications. Data is now a first-class construct that is exchanged over the web independently from presentation information. Astoria is both a set of patterns and an actual implementation of a programming interface for data in the Web. Astoria exposes data in a database over an HTTP interface and follows the REST architectural style for exploring and manipulating data. We demonstrate the URI patterns used by Astoria and their associated semantics, and the various data formats supported by the system. We explore specific requirements for the formats such as hyper-linking and usability for both retrieval and updates. We also demonstrate step by step how developers use Astoria to create custom data services on top of existing databases.

Towards Declarative Queries on Adaptive Data Structures

In this work we look at combining emerging technologies in programming languages with traditional query processing techniques to provide support for efficient execution of declarative queries over adaptive data structures. We first explore available technologies such as Language-Integrated Query, or LINQ (which enables declarative queries in programming languages) and the ADO.NET Data Set classes (which provide various efficient alternatives to manipulate data in procedural terms). Unfortunately, combining the good features in both technologies is not straightforward, since LINQ over Data Sets results by default in execution plans that do not exploit the specific characteristics of the data structures. To address this limitation, we introduce a lightweight optimizer that dynamically chooses appropriate execution strategies for declarative queries on Data Sets based on their internal structure. To further enable declarative programming, we introduce a component that dynamically reorganizes the internal representation of Data Sets, so that they automatically respond to workload changes. We experimentally showcase the features of our approach.