Anisoara Nica

The EVE approach: view synchronization in dynamic distributed environments

The construction and maintenance of data warehouses (views) in large-scale environments composed of numerous distributed and evolving information sources (ISs) such as the WWW has received great attention recently. Such environments are plagued with changing information because ISs tend to continuously evolve by modifying not only their content but also their query capabilities and interface and by joining or leaving the environment at any time. We are the first to introduce and address the problem of schema changes of ISs, while previous work in this area, such as incremental view maintenance, has mainly dealt with data changes at ISs. We outline our solution approach to this challenging new problem of how to adapt views in such evolving environments. We identify a new view adaptation problem for view evolution in the context of ISs schema changes, which we call view synchronization. We also outline the Evolvable View Environment (EVE) approach that we propose as framework for solving the view synchronization problem, along with our decisions concerning the key design issues surrounding EVE. The main contributions of this paper are: 1) we provide an E-SQL view definition language with which the view definer can direct the view evolution process, 2) we introduce a model for information source description which allows a large class of ISs to participate in our system dynamically, 3) we formally define what constitutes a legal view rewriting, 4) we develop replacement strategies for affected view components which are designed to meet the preferences expressed by E-SQL, 5) we prove the correctness of the replacement strategies, and 6) we provide a set of view synchronization algorithms based on those strategies. A prototype of our EVE system has successfully been built using Java, JDBC, Oracle, and MS Access.

The CVS Algorithm for View Synchronization in Evolvable Large-Scale Information Systems

Current view technology supports only static views in the sense that views become undefined and hence obsolete as soon as the underlying information sources (ISs) undergo capability changes. We propose to address this new view evolution problem — which we call view synchronization — by a novel solution approach that allows affected view definitions to be dynamically evolved to keep them in synch with evolving ISs. We present in this paper a general strategy for the view synchronization process that guided by constraints imposed by the view evolution preferences embedded in the view definition achieves view preservation (i.e., view redefinition). We present the formal correctness, the CVS algorithm, as well as numerous examples to demonstrate the main concepts.

View maintenance after view synchronization

While current view technology assumes that information systems (ISs) do not change their schemas, our Evolvable View Environment (EVE) project addresses this problem by evolving the view definitions affected by IS schema changes, which we call view synchronization. In EVE, the view synchronizer rewrites the view definitions by replacing view components with suitable components from other ISs. However, after such a view redefinition process, the view extents, if materialized, must also be brought up to date. In this paper, we propose strategies to address this incremental adaptation of the view extent after view synchronization. One key idea of our approach is to regard the complex changes done to a view definition after synchronization as an atomic unit; another is to exploit knowledge of how the view definition was synchronized, especially the containment information between the old and new views. Our techniques would successfully adapt views under the unavailability of base relations, while currently known maintenance strategies from the literature would fail.

FPTree: A Hybrid SCM-DRAM Persistent and Concurrent B-Tree for Storage Class Memory

The advent of Storage Class Memory (SCM) is driving a rethink of storage systems towards a single-level architecture where memory and storage are merged. In this context, several works have investigated how to design persistent trees in SCM as a fundamental building block for these novel systems. However, these trees are significantly slower than DRAM-based counterparts since trees are latency-sensitive and SCM exhibits higher latencies than DRAM. In this paper we propose a novel hybrid SCM-DRAM persistent and concurrent B-Tree, named Fingerprinting Persistent Tree (FPTree) that achieves similar performance to DRAM-based counterparts. In this novel design, leaf nodes are persisted in SCM while inner nodes are placed in DRAM and rebuilt upon recovery. The FPTree uses Fingerprinting, a technique that limits the expected number of in-leaf probed keys to one. In addition, we propose a hybrid concurrency scheme for the FPTree that is partially based on Hardware Transactional Memory. We conduct a thorough performance evaluation and show that the FPTree outperforms state-of-the-art persistent trees with different SCM latencies by up to a factor of 8.2. Moreover, we show that the FPTree scales very well on a machine with 88 logical cores. Finally, we integrate the evaluated trees in memcached and a prototype database. We show that the FPTree incurs an almost negligible performance overhead over using fully transient data structures, while significantly outperforming other persistent trees.

SQL Anywhere: A Holistic Approach to Database Self-management

In this paper we present an overview of the self-management features of SQL anywhere, a full-function relational database system designed for frontline business environments with minimal administration. SQL Anywhere can serve as a high-performance workgroup server, an embedded database that is installed along with an application, or as a mobile database installed on a handheld device that provides full database services, including two-way synchronization, to applications when the device is disconnected from the corporate intranet. We illustrate how the various self-management features work in concert to provide a robust data management solution in zero-administration environments.

Evolvable view environment (EVE): non-equivalent view maintenance under schema changes

Supporting independent ISs and integrating them in distributed data warehouses (materialized views) is becoming more important with the growth of the WWW. However, views defined over autonomous ISs are susceptible to schema changes. In the <italic>EVE</italic> project we are developing techniques to support the maintenance of data warehouses defined over distributed <italic>dynamic</italic> ISs [5, 6, 7]. The <italic>EVE</italic> system is the first to allow views to survive <italic>schema changes</italic> of their underlying ISs while also adapting to changing data in those sources. <italic>EVE</italic> achieves this is two steps: applying view query rewriting algorithms that exploit information about alternative ISs and the information they contain, and incrementally adapting the view extent to the view definition changes. Those processes are referred to as <italic>view synchronization</italic> and <italic>view adaption</italic>, respectively. They increase the survivability of materialized views in changing environments and reduce the necessity of human interaction in system maintenance.

Data warehouses evolution: trade-offs between quality and cost of query rewritings

Query rewriting with relaxed semantics has been proposed as a means of retaining the validity of a data warehouse (i.e., materialized queries) in a changing environment. Attributes in the query interface can be classified as essential or dispensable (if it cannot be retained) according to the query definers preferences. Similarly, preferences for query extent can be specified, for example, to indicate whether a subset of the original result is acceptable or not. The paper discusses the trade-off between quality and cost of query rewriting.

Using containment information for view evolution in dynamic distributed environments

The maintenance of materialized views in large scale environments composed of numerous information sources (ISs), such as in the WWW, is complicated by ISs not only continuously modifying their contents bur also their capabilities (schemas and query interfaces). With current view technology, views become undefined when ISs change their capabilities. Our Evolvable View Environment (EVE) project addresses this new problem of evolving views under IS capabilities changes, which we call the view synchronization problem. Key principles of EVE include a user specified preference model for view evolution (Evolvable-SQL (E-SQL)) and a Model for Information Source Descriptions (MISD). We present a formal characterization of correctness of view synchronization using containment constraints defined in MISD. Then, we give a novel view synchronization algorithm for view rewriting, exploiting general containment constraints between the to-be-replaced relation and its replacement.

Quality-driven processing of sliding window aggregates over out-of-order data streams

One fundamental challenge in data stream processing is to cope with the ubiquity of disorder of tuples within a stream caused by network latency, operator parallelization, merging of asynchronous streams, etc. High result accuracy and low result latency are two conflicting goals in out-of-order stream processing. Different applications may prefer different extent of trade-offs between the two goals. However, existing disorder handling solutions either try to meet one goal to the extreme by sacrificing the other, or try to meet both goals but have shortcomings including unguaranteed result accuracy or increased complexity in operator implementation and application logic. To meet different application requirements on the latency versus result accuracy trade-off in out-of-order stream processing, in this paper, we propose to make this trade-off user-configurable. Particularly, focusing on sliding window aggregates, we introduce AQ-K-slack, a buffer-based quality-driven disorder handling approach. AQ-K-slack leverages techniques from the fields of sampling-based approximate query processing and control theory. It can adjust the input buffer size dynamically to minimize the result latency, while respecting user-specified threshold on relative errors in produced query results. AQ-K-slack requires no a priori knowledge of disorder characteristics of data streams, and imposes no changes to the query operator implementation or the application logic. Experiments over real-world out-of-order data streams show that, compared to the state-of-art, AQ-K-slack can reduce the average buffer size, thus the average result latency, by at least 51% while respecting user-specified requirement on the accuracy of query results.

Exploiting ordered dictionaries to efficiently construct histograms with q-error guarantees in SAP HANA

Histograms that guarantee a maximum multiplicative error (q-error) for estimates may significantly improve the plan quality of query optimizers. However, the construction time for histograms with maximum q-error was too high for practical use cases. In this paper we extend this concept with a threshold, i.e., an estimate or true cardinality θ, below which we do not care about the q-error because we still expect optimal plans. This allows us to develop far more efficient construction algorithms for histograms with bounded error. The test for θ, q-acceptability developed also exploits the order-preserving dictionary encoding of SAP HANA. We have integrated this family of histograms into SAP HANA, and we report on the construction time, histograms size, and estimation errors on real-world data sets. In virtually all cases the histograms can be constructed in far less than one second, requiring less than 5% of space compared to the original compressed data.