Data is dynamically structured by nature and can be highly diverse and multifaceted. Often, such diverse and complex information needs to be linked. Conventional data-stores, such as relational databases, do not conveniently accommodate dynamically varying structures, as frequently modifying database schemas is not feasible. RDF triplestores offer a flexible solution for handling such data, where any property about an entity can be described by a triple having a subject, a predicate, and an object. Also, data is inherently distributed due to origination points, ownership and many other reasons. Furthermore, storing data in triplestores gives rise to the need to distribute data due to the large number of triples that would result by migrating existing data from a database, for example. In this paper, we present our work on designing index structures in order to facilitate efficient querying of a distributed triplestore (DTS). The distributed querying algorithm in DTS makes use of a sub-graph isomorphism approach to eliminate traversing edges between triplestores that does not have the potential to produce any results. We show that our triplestore has equivalent performance as 3Store when used in a non-distributed mode. Our performance tests in the distributed mode show that the indexes improve efficiency of querying.

Efficient serialization and deserialization of data is a fundamental operation of many grid systems. Some serializers are message-based, while others are stream-based. Streaming serializers can be more scalable and flexible, by promoting free form conversations not fixed to any particular static structure. We present the design and implementation of the XBS binary serializer, focusing on three important features: efficient serialization of large and small arrays, efficient pass-through of opaque data by grid intermediaries, and efficient representation of numbers with a large dynamic range. The first feature is important because such arrays dominate scientific computing. The second feature is useful for grid intermediaries such as gateways and proxies, which are becoming ever more important as grid systems become more complex. The third feature is important for efficiently supporting systems without arbitrary size limits. XBS is a freely-available C++ library and provides an object-oriented API, based on generic programming techniques.

XML provides flexible, extensible data models and type systems for structured data, and has found wide-acceptance in many domains. XML processing can be slow, however, especially for scientific data, thus leading to the conventional wisdom that XML is not appropriate for such data. Instead, data is stored in specialized binary formats, and is transmitted via work-arounds such as attachments and base64 encoding. Though these work-arounds can be useful, they nonetheless relegate scientific data to second-class status within the Web services framework; and they generally require yet another API, data model, and type system. An alternative solution is to use more efficient encodings of XML, often known as "binary XML". Using XML uniformly throughout an application simplifies and unifies design and development. In this paper we present a binary XML format and implementation for scientific data called Binary XML for Scientific Applications (BXSA). We show that performance is comparable to that of commonly used scientific data formats such as netCDF. These results challenge the prevailing practice of handling control and data separately in scientific applications, with Web services for control and specialized binary formats for data

Instruments and sensors and their accompanying actuators are essential to the conduct of scientific research. In many cases they provide observations in electronic format and can be connected to computer networks with varying degrees of remote interactivity. These devices vary in their architectures and type of data they capture and may generate data at various rates. In this paper we present an overview of the design goals and initial implementation of the common instrument middleware architecture (CIMA), a framework for making instruments and sensors network accessible in a standards-based, uniform way, and for interacting remotely with instruments and the data they produce. Some of the issues CIMA addresses include: flexibility in network transport, efficient and high throughput data transport, the availability (or lack of) computational, storage and networking resources at the instrument or sensor platform, evolution of instrument design, and reuse of data acquisition and processing codes