Schema profiling of document-oriented databases

Abstract

In document-oriented databases, schema is a soft concept and the documents in a collection can be stored using different local schemata. This gives designers and implementers augmented flexibility; however, it requires an extra effort to understand the rules that drove the use of alternative schemata when sets of documents with different -and possibly conflicting-schemata are to be analyzed or integrated. In this paper we propose a technique, called schema profiling, to explain the schema variants within a collection in document-oriented databases by capturing the hidden rules explaining the use of these variants. We express these rules in the form of a decision tree (schema profile). Consistently with the requirements we elicited from real users, we aim at creating explicative, precise, and concise schema profiles. The algorithm we adopt to this end is inspired by the well-known C4.5 classification algorithm and builds on two original features: the coupling of value-based and schema-based conditions within schema profiles, and the introduction of a novel measure of entropy to assess the quality of a schema profile. A set of experimental tests made on both synthetic and real datasets demonstrates the effectiveness and efficiency of our approach.

Approximating the Schema of a Set of Documents by Means of Resemblance

“…[4] also identifies the smallest set of core attributes, but their approach is more complex and computationally expensive than the one we present here. Finally, [28] goes a step further by not finding a common schema, but trying to explain the different variants found in documents by means of association rules.…”

Inferring Restricted Regular Expressions with Interleaving from Positive and Negative Samples

“…Studying the inference of RE(&) has several practical motivations, such as schema inference. The presence of a schema for XML documents has many advantages, such as for query processing and optimization, data integration and exchange [11,30]. However, many XML documents in practice are not accompanied by a valid schema [16], making schema inference an attractive research topic [2,3,10,14,31].…”

Approximate OLAP of document-oriented databases: A variety-aware approach

“…Recent years have witnessed an erosion of the relational DBMS predominance to the benefit of DBMSs based on alternative representation models (e.g., document-oriented and graph-based) which adopt a schemaless representation for data. Schemaless databases are preferred to relational ones for storing heterogeneous data with variable schemas and structural forms; typical schema variants within a collection consist in missing or additional fields, in different names or types for an field, and in different structures for instances [1]. The absence of a unique schema grants flexibility to operational applications but adds complexity to analytical applications, in which a single analysis often involves large sets of data with different schemas.…”

“…In this paper we propose an original approach to multidimensional querying and OLAP on schemaless sources, in particular on collections stored in document-oriented databases (DODs) such as MongoDB 1 . The basic idea is to stop fighting against data heterogeneity and schema variety, and welcome it as an inherent source of information wealth in schemaless sources.…”