We describe a method of inferring join plans for a set of relation instances, in the absence of any metadata, such as attribute domains, attribute names, or constraints (e.g., keys or foreign keys). Our method enumerates the possible join plans in order of likelihood, based on the compatibility of a pair of columns and their suitability as join attributes (i.e. their appropriateness as keys). We outline two variants of the approach. The first variant is accurate but potentially time-consuming, especially for large relations that do not fit in memory. The second variant is an approximation of the former and hence less accurate, but is considerably more efficient, allowing the method to be used online, even for large relations. We provide experimental results showing how both forms scale in terms of performance as the number of candidate join attributes and the size of the relations increase. We also characterize the accuracy of the approximate variant with respect to the exact variant.

Object-Relational database management system is an integrated hybrid cooperative approach to combine the best practices of both the relational model utilizing SQL queries and the object-oriented, semantic paradigm for supporting complex data creation. In this paper, a highly scalable, information

into structured one by introducing schema construction methodology, compared to the former studies which are fully dependent on schemaless manipulations. For schema construction, we defined operations for building IS-A/IS-PART-OF relationships, collecting data objects to build a primitive class, and merging

the Semantic Web have led to a rapid increase in both the quantity as well as the variety of Web applications that rely on the SPARQL interface to query RDF data. Thus, RDF data management systems are increasingly exposed to workloads that are far more diverse and dynamic than what these systems

Object-Relational database management system is an integrated hybrid cooperative approach to combine the best practices of both the relational model utilizing SQL queries and the objectoriented, semantic paradigm for supporting complex data creation. In this paper, a highly scalable, information

Abstract. Schema-less change detection is the processes of comparing successive versions of an XML document or data collection to determine which portions are the same and which have changed, without using a schema. Change detection can be used to reduce space in an historical data collection

Information overload is a common symptom in the Internet age nowadays. Search engines assist users to seek a "needle in a haystack". However, the evolving demand of data intensive applications now asks for not only an isolated piece of information, but also a collection of interlinked

Graph has become increasingly important in modelling complicated structures and schemaless data such as proteins, chemical compounds, and XML documents. Given a graph query, it is desirable to retrieve graphs quickly from a large database via graph-based indices. In this paper, we investigate

Object-Relational database management system is an integrated hybrid cooperative approach to combine the best practices of both the relational model utilizing SQL queries and the object-oriented, semantic paradigm for supporting complex data creation. In this paper, a highly scalable, information

Abstract. JSON has become a very popular lightweigth format for data ex-change. JSON is human readable and easy for computers to parse and use. How-ever, JSON is schemaless. Though this brings some benefits (e.g., flexibility in the representation of the data) it can become a problem when consuming