We develop a new schema for unstructured data. Traditional schemas resemble the type systems of programming languages. For unstructured data, however, the underlying type may be much less constrained and hence an alternative way of expressing constraints on the data is needed. Here, we propose

A new kind of data model has recently emerged in which the database is not constrained by a conventional schema. Systems like ACeDB, which has become very popular with biologists, and the recent Tsimmis proposal for data integration organize data in tree-like structures whose components can be used

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ions and Unstructuring Data Structures Christian Collberg Clark Thomborson Douglas Low Department of Computer Science, The University of Auckland, Private Bag 92019, Auckland, New Zealand. fcollberg,cthombor,dlow001g@cs.auckland.ac.nz Abstract To ensure platform independence, mobile programs

Abstract — Large volumes of data that will be stored and accessed in future is unstructured. The unstructured data is generated in a very fast pace and uses large storage areas. This increases the storage budget. Extracting value from this unstructured data which balances the budget is the most

There is growing number of applications that require access to both structured and unstructured data. Such collections of data have been referred to as dataspaces, and Dataspace Support Platforms (DSSPs) were proposed to offer several services over dataspaces, including search and query, source

In this paper we study the problem of protecting privacy in the publication of set-valued data. Con-sider a collection of transactional data that contains detailed information about items bought together by individuals. Even after removing all personal characteristics of the buyer, which can serve

-anonymity for set-valued data as a variant of the k-anonymity model for traditional relational datasets. We define an optimization problem that arises from this definition of anonymity and provide O(k log k) and O(1)-approximation algorithms for the same. We demonstrate applicability of our algorithms

We propose an approach to interpreting a structured query language over unstructured data. We define partial records on heterogeneous relations as a means to bridge the gap between structured and unstructured data, as well as an algebra that merges relational operations with Information Extraction

Both structured and unstructured data, as well as structured data representing several different types of tuples, may be integrated into a single list for browsing or retrieval. Data may be arranged in the Gray code order of the features and metadata, producing optimal ordering for browsing. We