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Database access logs are the starting point for many forms of database administration, from database performance tuning, to security auditing, to benchmark design, and many more. Unfortunately, query logs are also large and unwieldy, and it can be difficult for an analyst to extract broad patterns from the set of queries found therein. Clustering is a natural first step towards understanding the massive query logs. However, many clustering methods rely on the notion of pairwise similarity, which is challenging to compute for SQL queries, especially when the underlying data and database schema is unavailable. We investigate the problem of computing similarity between queries, relying only on the query structure. We conduct a rigorous evaluation of three query similarity heuristics proposed in the literature applied to query clustering on multiple query log datasets, representing different types of query workloads. To improve the accuracy of the three heuristics, we propose a generic feature engineering strategy, using classical query rewrites to standardize query structure. The proposed strategy results in a significant improvement in the performance of all three similarity heuristics.

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user's querying behavior and finds matching patterns in the system's query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user's session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach.

In most current database systems, data is updated in-place. In order to support recovery and increase performance, write-ahead logging is used. This logging defers the in-place updates. However, sooner or later, the updates have to be applied to the database. Even if this is done as a batch operation, it can result in many nonsequential writes. In order to avoid this, another approach is to eliminate the database completely and use a log-only approach. In this case, the log is written contiguously to the disk, in a no-overwrite way using large blocks. When using the log-only approach keeping previous versions comes almost for free, and this approach is therefore particularly interesting for transaction-time temporal object database systems. Although the log-only approach in its basic form is relatively straightforward, it is not trivial to support features such as steal/no-force buffer management, fuzzy checkpointing, and fast commit. We describe, in detail, algorithms and strategies for object and log management that make support for these features possible.

Increasingly, for extensibility and performance, special purpose application code is being integrated with database system code. Such application code has direct access to database system buffers, and as a result, the danger of data being corrupted due to inadvertent application writes is increased. Previously proposed hardware techniques to protect from corruption require system calls, and their performance depends on details of the hardware architecture. We investigate an alternative approach which uses codewords associated with regions of data to detect corruption and to prevent corrupted data from being used by subsequent transactions. We develop several such techniques which vary in the level of protection, space overhead, performance, and impact on concurrency. These techniques are implemented in the Dali main-memory storage manager, and the performance impact of each on normal processing is evaluated. Novel techniques are developed to recover when a transaction has read corrupted data caused by a bad write and gone on to write other data in the database. These techniques use limited and relatively low-cost logging of transaction reads to trace the corruption and may also prove useful when resolving problems caused by incorrect data entry and other logical errors., © Elsevier

Hyperlog is a declarative, graph based language that supports database querying and update. It visualizes schema information, data, and query output as sets of nested graphs, which can be stored, browsed, and queried in a uniform way. Thus, the user need only be familiar with a very small set of syntactic constructs. Hyperlog queries consist of a set of graphs that are matched against the database. Database updates are supported by means of programs consisting of a set of rules. The paper discusses the formulation, evaluation, expressiveness, and optimization of Hyperlog queries and programs. We also describe a prototype implementation of the language and we compare and contrast our approach with work in a number of related areas, including visual database languages, graph based data models, database update languages, and production rule systems.

A heterogeneous distributed database environment integrates a set of autonomous database systems to provide global database functions. A flexible transaction approach has been proposed for the heterogeneous distributed database environments. In such an environment, flexible transactions can increase the failure resilience of global transactions by allowing alternate (but in some sense equivalent) executions to be attempted when a local database system fails or some subtransactions of the global transaction abort. We study the impact of compensation, retry, and switching to alternative executions on global concurrency control for the execution of flexible transactions. We propose a new concurrency control criterion for the execution of flexible and local transactions, termed F-serializability, in the error-prone heterogeneous distributed database environments. We then present a scheduling protocol that ensures F-serializability on global schedules. We also demonstrate that this scheduler avoids unnecessary aborts and compensation.

Due to recent developments in network technologies, broader channel bandwidth is becoming prevalent in worldwide networks. As one of the new technologies making good use of such broadband channels, dynamic relocation of databases through networks, database migration, will soon be used in practice as a powerful and basic database operation. We propose two transaction processing methods to take advantage of database migration in broadband networks. These methods choose the most efficient transaction processing method between the conventional method, based on the two-phase commit protocol, and our method, using database migration. We also propose a concurrency control mechanism and a recovery mechanism for our proposed methods. Simulation results are presented comparing the performance of our proposed methods and the conventional transaction processing method based on the two-phase commit protocol. The results demonstrate that the effective use of database migration produces better performance than the conventional method.

An approach to organizing storage in database systems is presented that, under a wide range of conditions, saves both storage space and processing time. Text values in a database are replaced by short, fixed-length, rank-preserving numeric tokens. The actual values are stored in separate, non-redundant storage. Database operations that depend only on the relative magnitude of data values can be performed directly on the tokens. Tokenization is shown to improve database performance most in situations where there are a lot of ad hoc queries and a low volume of database insertions relative to other operations.

The research in materialization of derived data elements has dealt so far with the if issue of whether to physically store derived data elements. In the active database area, there has been some research on the how issue. We deal with the when issue, devising an optimization model to determine the optimal materialization strategy. The decision problem confronted by the optimization model deals with devising the materialization strategy that consists of a set of interdependent decisions about each derived data element. Each decision relates to two issues: Should the value of a derived data element be persistent? What is the required level of consistency of a derived value with respect to its derivers? For each derived data element, the decision is based on both its local properties and its interdependencies with other derived values. The optimization model is based on a heuristic algorithm that finds a local optimum in O(N/sup 2/) and a monitor that obtains feedback about the actual database performance. This optimization model is general and is not specific to any data model. Our experimental results show that a predictor for the optimal solution cannot be obtained in any intuitive or analytic way, due to the complexity of the involved considerations; thus, there is no obvious way to achieve these results without using the optimization model. This fact is a strong motivation for applying such an optimization model.

In a heterogeneous database system, a query for one type of database system (i.e., a source query) may have to be translated to an equivalent query (or queries) for execution in a different type of database system (i.e., a target query). Usually, for a given source query, there is more than one possible target query translation. Some of them can be executed more efficiently than others by the receiving database system. Developing a translation procedure for each type of database system is time-consuming and expensive. We abstract a generic hierarchical database system (GHDBS) which has properties common to database systems whose schema contains hierarchical structures (e.g., System 2000, IMS, and some object-oriented database systems). We develop principles of query translation with GHDBS as the receiving database system. Translation into any specific system can be accomplished by a translation into the general system with refinements to reflect the characteristics of the specific system. We develop rules that guarantee correctness of the target queries, where correctness means that the target query is equivalent to the source query. We also provide rules that can guarantee a minimum number of target queries in cases when one source query needs to be translated to multiple target queries. Since the minimum number of target queries implies the minimum number of times the underlying system is invoked, efficiency is taken into consideration. >

Transaction time is used for time stamping object values to record their database history and formulate a zero information loss model for database transactions. The model consists of three components, a data history store, an update store, and a query store. In such a model, the effect of a past transaction (a query or an update) can be determined at any time. Additionally, the update and query stores make it possible to reconstruct the circumstances of updates and the information divulged in queries. Such a model is suitable for the design of secure, easy-to-audit database systems. >