Query Scalability Research Articles

Supporting secure keyword search in the personal cloud

The Personal Cloud paradigm has emerged as a solution that allows individuals to manage under their control the collection, usage and sharing of their data. However, by regaining the full control over their data, the users also inherit the burden of protecting it against all forms of attacks and abusive usages. The Secure Personal Cloud architecture relieves the individual from this security task by employing a secure token (i.e., a tamper-resistant hardware device) to control all the sensitive information (e.g., encryption keys, metadata, indexes) and operations (e.g., authentication, data encryption/decryption, access control, and query processing). However, secure tokens are usually equipped with extremely low RAM but have significant Flash storage capacity (Gigabytes), which raises important barriers for embedded data management. This paper presents a new embedded search engine specifically designed for secure tokens, which applies to the important use-case of managing and securing documents in the Personal Cloud context. Conventional search engines privilege either insertion or query scalability but cannot meet both requirements at the same time. Moreover, very few solutions support data deletions and updates in this context. In this paper, we introduce three design principles, namely Write-Once Partitioning, Linear Pipelining and Background Linear Merging, and show how they can be combined to produce an embedded search engine matching the hardware constraints of secure tokens and reconciling high insert/delete/update rate and query scalability. Our experimental results, obtained with a prototype running on a representative hardware platform, demonstrate the scalability of the approach on large datasets and its superiority compared to state of the art methods. Finally, we also discuss the integration of our solution in another important real use-case related to performing information retrieval in smart objects.

Scaling up the performance of more powerful Datalog systems on multicore machines

Extending RDBMS technology to achieve performance and scalability for queries that are much more powerful than those of SQL-2 has been the goal of deductive database research for more than thirty years. The $$\mathcal {D}e\mathcal {A}\mathcal {L}\mathcal {S}$$DeALS system has made major progress toward this goal, by (1) Datalog extensions that support the more powerful recursive queries needed in advanced applications, and (2) superior performance for both traditional recursive queries and those made possible by the new extensions, while (3) delivering competitive performance with commercial RDBMSs on non-recursive queries. In this paper, we focus on the techniques used to support the in-memory evaluation of Datalog programs on multicore machines. In $$\mathcal {D}e\mathcal {A}\mathcal {L}\mathcal {S}$$DeALS, a Datalog program is represented as an AND/OR tree, and multiple copies of the same AND/OR tree are used to access the tables in the database concurrently during the parallel evaluation. We describe compilation techniques that (1) recognize when the given program is lock-free, (2) transform a locking program into a lock-free program, and (3) find an efficient parallel plan that correctly evaluates the program while minimizing the use of locks and other overhead required for parallel evaluation. Extensive experiments demonstrate the effectiveness of the proposed techniques.

A scalable search engine for mass storage smart objects

This paper presents a new embedded search engine designed for smart objects. Such devices are generally equipped with extremely low RAM and large Flash storage capacity. To tackle these conflicting hardware constraints, conventional search engines privilege either insertion or query scalability but cannot meet both requirements at the same time. Moreover, very few solutions support document deletions and updates in this context. In this paper, we introduce three design principles, namely Write-Once Partitioning, Linear Pipelining and Background Linear Merging, and show how they can be combined to produce an embedded search engine reconciling high insert/delete/update rate and query scalability. We have implemented our search engine on a development board having a hardware configuration representative for smart objects and have conducted extensive experiments using two representative datasets. The experimental results demonstrate the scalability of the approach and its superiority compared to state of the art methods.

A Framework for Supporting Tree-Like Indexes on the Chord Overlay

With the explosive growth of data, to support efficient data management including queries and updates, the database system is expected to provide tree-like indexes, such as R-tree, M-tree, B+-tree, according to different types of data. In the distributed environment, the indexes have to be scattered across the compute nodes to improve reliability and scalability. Indexes can speed up queries, but they incur maintenance cost when updates occur. In the distributed environment, each compute node maintains a subset of an index tree, so keeping the communication cost small is more crucial, or else it occupies lots of network bandwidth and the scalability and availability of the database system are affected. Further, to achieve the reliability and scalability for queries, several replicas of the index are needed, but keeping the replicas consistent is not straightforward. In this paper, we propose a framework supporting tree-like indexes, based on Chord overlay, which is a popular P2P structure. The framework dynamically tunes the number of replicas of index to balance the query cost and the update cost. Several techniques are designed to improve the efficiency of updates without the cost of performance of the queries. We implement M-tree and R-tree in our framework, and extensive experiments on real- life and synthetic datasets verify the efficiency and scalability of our framework.