XML is quickly becoming the de facto standard for data exchange over the Internet. This is creating a new set of data management requirements involving XML, such as the need to store and query XML documents. Researchers have proposed using relational database systems to satisfy these requirements by devising ways to "shred" XML documents into relations, and translate XML queries into SQL queries over these relations. However, a key issue with such an approach, which has largely been ignored in the research literature, is how (and whether) the ordered XML data model can be efficiently supported by the unordered relational data model. This paper shows that XML's ordered data model can indeed be efficiently supported by a relational database system. This is accomplished by encoding order as a data value. We propose three order encoding methods that can be used to represent XML order in the relational data model, and also propose algorithms for translating ordered XPath expressions into SQL using these encoding methods. Finally, we report the results of an experimental study that investigates the performance of the proposed order encoding methods on a workload of ordered XML queries and updates.

http://db.ucsd.edu/static/cse232b-archive/papers/tatarinov02.pdf

Storing and querying ordered XML using a relational database system

The collection of digital information by governments, corporations, and individuals has created tremendous opportunities for knowledge- and information-based decision making. Driven by mutual benefits, or by regulations that require certain data to be published, there is a demand for the exchange and publication of data among various parties. Data in its original form, however, typically contains sensitive information about individuals, and publishing such data will violate individual privacy. The current practice in data publishing relies mainly on policies and guidelines as to what types of data can be published and on agreements on the use of published data. This approach alone may lead to excessive data distortion or insufficient protection. Privacy-preserving data publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. Recently, PPDP has received considerable attention in research communities, and many approaches have been proposed for different data publishing scenarios. In this survey, we will systematically summarize and evaluate different approaches to PPDP, study the challenges in practical data publishing, clarify the differences and requirements that distinguish PPDP from other related problems, and propose future research directions.

/pdf/privacy-preserving-data-publishing-a-survey-of-recent-4o22vc981u.pdf

Privacy-preserving data publishing: A survey of recent developments

Online applications are vulnerable to theft of sensitive information because adversaries can exploit software bugs to gain access to private data, and because curious or malicious administrators may capture and leak data. CryptDB is a system that provides practical and provable confidentiality in the face of these attacks for applications backed by SQL databases. It works by executing SQL queries over encrypted data using a collection of efficient SQL-aware encryption schemes. CryptDB can also chain encryption keys to user passwords, so that a data item can be decrypted only by using the password of one of the users with access to that data. As a result, a database administrator never gets access to decrypted data, and even if all servers are compromised, an adversary cannot decrypt the data of any user who is not logged in. An analysis of a trace of 126 million SQL queries from a production MySQL server shows that CryptDB can support operations over encrypted data for 99.5% of the 128,840 columns seen in the trace. Our evaluation shows that CryptDB has low overhead, reducing throughput by 14.5% for phpBB, a web forum application, and by 26% for queries from TPC-C, compared to unmodified MySQL. Chaining encryption keys to user passwords requires 11--13 unique schema annotations to secure more than 20 sensitive fields and 2--7 lines of source code changes for three multi-user web applications.

/pdf/cryptdb-protecting-confidentiality-with-encrypted-query-4gyzgz1i1f.pdf

CryptDB: protecting confidentiality with encrypted query processing

Increasingly pervasive networks are leading towards a world where data is constantly in motion. In such a world, conventional techniques for query processing, which were developed under the assumption of a far more static and predictable computational environment, will not be sufficient. Instead, query processors based on adaptive dataflow will be necessary. The Telegraph project has developed a suite of novel technologies for continuously adaptive query processing. The next generation Telegraph system, called TelegraphCQ, is focused on meeting the challenges that arise in handling large streams of continuous queries over high-volume, highly-variable data streams. In this paper, we describe the system architecture and its underlying technology, and report on our ongoing implementation effort, which leverages the PostgreSQL open source code base. We also discuss open issues and our research agenda.

http://db.csail.mit.edu/madden/html/TCQcidr03.pdf

TelegraphCQ: Continuous Dataflow Processing for an Uncertain World.

DISCOVER operates on relational databases and facilitates information discovery on them by allowing its user to issue keyword queries without any knowledge of the database schema or of SQL. DISCOVER returns qualified joining networks of tuples, that is, sets of tuples that are associated because they join on their primary and foreign keys and collectively contain all the keywords of the query. DISCOVER proceeds in two steps. First the Candidate Network Generator generates all candidate networks of relations, that is, join expressions that generate the joining networks of tuples. Then the Plan Generator builds plans for the efficient evaluation of the set of candidate networks, exploiting the opportunities to reuse common subexpressions of the candidate networks.

We prove that DISCOVER finds without redundancy all relevant candidate networks, whose size can be data bound, by exploiting the structure of the schema. We prove that the selection of the optimal execution plan (way to reuse common subexpressions) is NP-complete. We provide a greedy algorithm and we show that it provides near-optimal plan execution time cost. Our experimentation also provides hints on tuning the greedy algorithm.

/pdf/discover-keyword-search-in-relational-databases-33fmpg1hdg.pdf

Discover: keyword search in relational databases

Complex queries are becoming commonplace, with the growing use of decision support systems. These complex queries often have a lot of common sub-expressions, either within a single query, or across multiple such queries run as a batch. Multiquery optimization aims at exploiting common sub-expressions to reduce evaluation cost. Multi-query optimization has hither-to been viewed as impractical, since earlier algorithms were exhaustive, and explore a doubly exponential search space.In this paper we demonstrate that multi-query optimization using heuristics is practical, and provides significant benefits. We propose three cost-based heuristic algorithms: Volcano-SH and Volcano-RU, which are based on simple modifications to the Volcano search strategy, and a greedy heuristic. Our greedy heuristic incorporates novel optimizations that improve efficiency greatly. Our algorithms are designed to be easily added to existing optimizers. We present a performance study comparing the algorithms, using workloads consisting of queries from the TPC-D benchmark. The study shows that our algorithms provide significant benefits over traditional optimization, at a very acceptable overhead in optimization time.

/pdf/efficient-and-extensible-algorithms-for-multi-query-4nip9bxol7.pdf

Efficient and extensible algorithms for multi query optimization

Current day database applications, with large numbers of users, require fine-grained access control mechanisms, at the level of individual tuples, not just entire relations/views, to control which parts of the data can be accessed by each user. Fine-grained access control is often enforced in the application code, which has numerous drawbacks; these can be avoided by specifying/enforcing access control at the database level. We present a novel fine-grained access control model based on authorization views that allows "authorization-transparent" querying; that is, user queries can be phrased in terms of the database relations, and are valid if they can be answered using only the information contained in these authorization views. We extend earlier work on authorization-transparent querying by introducing a new notion of validity, conditional validity. We give a powerful set of inference rules to check for query validity. We demonstrate the practicality of our techniques by describing how an existing query optimizer can be extended to perform access control checks by incorporating these inference rules.

https://www.cse.iitb.ac.in/~sudarsha/Pubs-dir/nontruman-sigmod04.pdf

Extending query rewriting techniques for fine-grained access control

Complex queries are becoming commonplace, with the growing use of decision support systems. These complex queries often have a lot of common sub-expressions, either within a single query, or across multiple such queries run as a batch. Multi-query optimization aims at exploiting common sub-expressions to reduce evaluation cost. Multi-query optimization has hither-to been viewed as impractical, since earlier algorithms were exhaustive, and explore a doubly exponential search space. 
In this paper we demonstrate that multi-query optimization using heuristics is practical, and provides significant benefits. We propose three cost-based heuristic algorithms: Volcano-SH and Volcano-RU, which are based on simple modifications to the Volcano search strategy, and a greedy heuristic. Our greedy heuristic incorporates novel optimizations that improve efficiency greatly. Our algorithms are designed to be easily added to existing optimizers. We present a performance study comparing the algorithms, using workloads consisting of queries from the TPC-D benchmark. The study shows that our algorithms provide significant benefits over traditional optimization, at a very acceptable overhead in optimization time.

Efficient and Extensible Algorithms for Multi Query Optimization

As Web applications manipulate an increasing amount of XML, there is a growing interest in storing XML data in relational databases. Due to the mismatch between the complexity of XML's tree structure and the simplicity of flat relational tables, there are many ways to store the same document in an RDBMS, and a number of heuristic techniques have been proposed. These techniques typically define fixed mappings and do not take application characteristics into account. However, a fixed mapping is unlikely to work well for all possible applications. In contrast, LegoDB is a cost-based XML storage mapping engine that explores a space of possible XML-to-relational mappings and selects the best mapping for a given application. LegoDB leverages current XML and relational technologies: (1) it models the target application with an XML Schema, XML data statistics, and an XQuery workload; (2) the space of configurations is generated through XML-Schema rewritings; and (3) the best among the derived configurations is selected using cost estimates obtained through a standard relational optimizer. We describe the LegoDB storage engine and provide experimental results that demonstrate the effectiveness of this approach.

https://dsl.cds.iisc.ac.in/~course/TIDS/papers/legodb.pdf

From XML schema to relations: a cost-based approach to XML storage

Materialized views have been found to be very effective at speeding up queries, and are increasingly being supported by commercial databases and data warehouse systems. However, whereas the amount of data entering a warehouse and the number of materialized views are rapidly increasing, the time window available for maintaining materialized views is shrinking. These trends necessitate efficient techniques for the maintenance of materialized views.In this paper, we show how to find an efficient plan for the maintenance of a set of materialized views, by exploiting common subexpressions between different view maintenance expressions. In particular, we show how to efficiently select (a) expressions and indices that can be effectively shared, by transient materialization; (b) additional expressions and indices for permanent materialization; and (c) the best maintenance plan — incremental or recomputation — for each view. These three decisions are highly interdependent, and the choice of one affects the choice of the others. We develop a framework that cleanly integrates the various choices in a systematic and efficient manner. Our evaluations show that many-fold improvement in view maintenance time can be achieved using our techniques. Our algorithms can also be used to efficiently select materialized views to speed up workloads containing queries and updates.

https://www.cse.iitb.ac.in/~sudarsha/Pubs-dir/viewmaint-sigmod01.pdf

Prasan Roy

Papers

Efficient and extensible algorithms for multi query optimization

Extending query rewriting techniques for fine-grained access control

Efficient and Extensible Algorithms for Multi Query Optimization

From XML schema to relations: a cost-based approach to XML storage

Materialized view selection and maintenance using multi-query optimization