Historical data (also called long data) holds the key to understanding when facts are true. It is through long data that one can understand the trends that have developed in the past, form the audit trails needed for justification, and make predictions about the future. For searching, there is also increasing interest to develop search capabilities over long data. In this article, we first motivate the need to develop a time machine for information that will help people "look back" so as to "look forward". We will overview key ideas on three components (extraction, linking, and cleaning) that we believe are central to the development of any time machine for information. Finally, we conclude with our thoughts on what we believe are some interesting open research problems. This article is based on the material presented in a tutorial at VLDB 2015.

Linked Data on the web consists of over 1000 datasets from a variety of domains. They are queried with the SPARQL query language. There exist many implementations of SPARQL, and this rich ecosystem has demanded a precise specification and compliance tests. However, the SPARQL specification has grown in complexity, and it is increasingly difficult for developers to validate their implementations. In this paper, we present a declarative specification for SPARQL, based on relational logic. It describes SPARQL with just a few operators, and is executable: queries written in it can be directly executed against real datasets.

While much work has focused on efficient processing of Big Data, little work considers how to understand them. In this paper, we describe Helix, a system for guided exploration of Big Data. Helix provides a unified view of sources, ranging from spreadsheets and XML files with no schema, all the way to RDF graphs and relational data with well-defined schemas. Helix users explore these heterogeneous data sources through a combination of keyword searches and navigation of linked web pages that include information about the schemas, as well as data and semantic links within and across sources. At a technical level, the paper describes the research challenges involved in developing Helix, along with a set of real-world usage scenarios and the lessons learned.

We show that existing mature, relational optimizers can be exploited with a novel schema to give better performance for property graph storage and retrieval than popular noSQL graph stores. The schema combines relational storage for adjacency information with JSON storage for vertex and edge attributes. We demonstrate that this particular schema design has benefits compared to a purely relational or purely JSON solution. The query translation mechanism translates Gremlin queries with no side effects into SQL queries so that one can leverage relational query optimizers. We also conduct an empirical evaluation of our schema design and query translation mechanism with two existing popular property graph stores. We show that our system is 2-8 times better on query performance, and 10-30 times better in throughput on 4.3 billion edge graphs compared to existing stores.

In recent years the inverted lists evaluation model along with holistic stack-based algorithms have been established as the most prominent techniques for evaluating XML queries on large persistent XML data. In this framework, we are using materialized views for optimizing XML queries. We consider a novel approach which instead of materializing the answer of a view materializes exactly the inverted sublists that are necessary for computing the answer of the view. This originality allows storing view materializations as compressed bitmaps, a solution that minimizes the materialization space and empowers performing optimization operations as CPU-efficient bitwise operations. To realize the potential of bitmap materialized views in optimizing query performance, we define and address the following problem (view configuration problem): given an XML tree and its schema find a template of tree-pattern views (view configuration) such that: (a) the views of this configuration can answer all the queries that can be issued against the schema, (b) their materialization fits in the space provided, and (c) evaluating the queries using these views minimizes the overall query evaluation cost. We consider an instance of this problem for tree pattern queries. Our intension is to find view configurations whose materializations are small enough to be stored in main memory. We find two candidate solution configurations and we identify cases where views can be excluded from materialization in a configuration without affecting query performance. In order to compare our approach with an approach which also can support the optimization of every query on the schema, we implemented an improvement of a state-of-the-art approach which is based on structural indexes. Our experimental results show that our approach is stable, greatly improves evaluating queries without materialized views, outperforms the structural index approach on all test cases and is very close to the optimal. These results characterize our approach as the best candidate for supporting the optimization of queries in the framework of the inverted lists model.

The fundamental assumption for query rewriting in heterogeneous environments is that the mappings used for the rewriting are complete , i.e., every relation and attribute mentioned in the query is associated, through mappings, to relations and attributes in the schema of the source that the query is rewritten. In reality, it is rarely the case that such complete sets of mappings exist between sources, and the presence of partial mappings is the norm rather than the exception. So, practically, existing query answering algorithms fail to generate any rewriting in the majority of cases. The question is then whether we can somehow relax queries that cannot be rewritten as such (due to insufficient mappings), and whether we can identify the interesting query relaxations, given the mappings at hand. In this paper, we propose a technique to compute query relaxations of an input query that can be rewritten and evaluated in an environment of collaborating autonomous and heterogeneous data sources. We extend traditional techniques for query rewriting, and we propose both an exhaustive and an optimized heuristic algorithm to compute and evaluate these relaxations. Our technique works with input of any query similarity measure. The experimental study proves the effectiveness and efficiency of our technique.

Although RDF graph data often come with an associated schema, recent studies have proven that real RDF data rarely conform to their perceived schemas. Since a number of data management decisions, including storage layouts, indexing, and efficient query processing, use schemas to guide the decision making, it is imperative to have an accurate description of the structuredness of the data at hand (how well the data conform to the schema).In this paper, we have approached the study of the structuredness of an RDF graph in a principled way: we propose a framework for specifying structuredness functions, which gauge the degree to which an RDF graph conforms to a schema. In particular, we first define a formal language for specifying structuredness functions with expressions we call rules. This language allows a user to state a rule to which an RDF graph may fully or partially conform. Then we consider the issue of discovering a refinement of a sort (type) by partitioning the dataset into subsets whose structuredness is over a specified threshold. In particular, we prove that the natural decision problem associated to this refinement problem is NP-complete, and we provide a natural translation of this problem into Integer Linear Programming (ILP). Finally, we test this ILP solution with three real world datasets and three different and intuitive rules, which gauge the structuredness in different ways. We show that the rules give meaningful refinements of the datasets, showing that our language can be a powerful tool for understanding the structure of RDF data, and we show that the ILP solution is practical for a large fraction of existing data.

RDF has become recently a very popular data model used in a variety of applications and use cases in both academia and industry. Query processing and evaluation is a central component in data management in general and is, thus, unsurprisingly one of the most active areas of research in the field of RDF data management. In this chapter we provide an overview of query processing techniques for the RDF data model using different system architectures. We survey techniques for both centralized and distributed RDF stores, including peer-to-peer, federated and cloud-based systems.

The invention relates to a method and a system for spreadsheet schema extraction. Aspects of the present invention provide a tool for extracting schema from a spreadsheet. In an embodiment, a set of data that is stored in an uncataloged tabular format, such as a spreadsheet, is retrieved. The structure of the retrieved set of data is surveyed to determine the dataset schema thereof. Then, data elements within the dataset schema are analyzed to obtain information regarding the data elements. Based on dataset schema and the element information, an interface can be constructed that allows remote access to the set of data.