bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ubiquitous, self-tuning, scalable servers
Authors: Spiro Peter 
Published: November 2005

Hardware developments allow wonderful reliability and essentially limitless capabilities in storage, networks, memory, and processing power. Costs have dropped dramatically. PCs are becoming ubiquitous.
The features and scalability of DBMS software have advanced to the point where most commercial systems can solve virtually all OLTP and DSS requirements.
The Internet and application software packages allow rapid deployment and facilitate a broad range of solutions.
The PointCast network (abstract)
Authors: Ramakrishnan Satish , Dayal Vibha 
Published: November 2005

PointCast Inc, the inventor and leader in broadcast news via the Internet and corporate intranets was founded in 1992 to deliver news as it happens from leading sources such as CNN, the New York Times, Wall Street Journal Interactive Edition and more, directly to a viewer's computer screen.
The PointCast Network is an integrated client/server system. The system give users control of selecting kinds of information the client retrieves and, within limits, the frequency of those retrievals.
The system is divided into client and server segments, referred to as “PointCast Client” and “the DataCenter” respectively. PointCast client is a program that runs on the user's Internet-connected computer, and performs a number of functions in addition to retrieving information from the DataCenter.
The server side of the system, known as the PointCast DataCenter, supports the client by providing compelling content in a timely fashion. The Data Center is composed of multiple sites geographically distributed all over US, each carrying a number of industrial strength web servers called “PointServers”. The PointServers are highly customized and “infinitely” scalable to serve close to 200 million requests handled by the PointCast network in a day.
The PointCast network receives content from over 100 different sources via satellite links or over the internet. A cluster of servers in the Data Center run customized processes round the clock which assimilate data from various sources to index, format and store it in multiple content databases.

This presentation will describe the basic plumbing of the PointCast Network and how some of the challenges of establishing one of the busiest data centers in the world were addressed and implemented. It will focus on following issues:

fault tolerance

load balancing

achieving scalability through pre-caching on servers

packaging information to optimize internet bandwidth usage

minimizing data latency.
The multidimensional database system RasDaMan
Authors: Baumann P. , Dehmel A. , Furtado P. , Ritsch R. , Widmann N. 
Published: November 2005

RasDaMan is a universal — i.e., domain-independent — array DBMS for multidimensional arrays of arbitrary size and structure. A declarative, SQL-based array query language offers flexible retrieval and manipulation. Efficient server-based query evaluation is enabled by an intelligent optimizer and a streamlined storage architecture based on flexible array tiling and compression.
RasDaMan is being used in several international projects for the management of geo and healthcare data of various dimensionality.
SuperSQL
Authors: Toyama Motomichi 
Published: November 2005

SuperSQL is an extension of SQL that allows query results presented in various media for publishing and presentations with simple but sophisticated formatting capabilities. SuperSQL query can generate various kinds of materials, for example, a LaTeX source file to publish query results in a nested table, HTML or Java source files to present the result on WWW browsers, and other media including MS-Excel worksheet, Tcl/Tk, O2C, etc. O2C is a data manipulation language of O2 and thus useful to migrate data in a relational database to an object oriented database.
SuperSQL is meant to provide a theoretical and practical foundation for 4GL-type applications such as report writers and DB/WWW coordinators.
In this demonstration, we show how TFE reorganize the query results into various media in a universal way, first by grouping tuples according to an arbitrary tree structured schema, and by translating them with the constructors available in the target media.
Multi-dimensional selectivity estimation using compressed histogram information
Authors: Lee Ju-Hong , Kim Deok-Hwan , Chung Chin-Wan 
Published: November 2005

The database query optimizer requires the estimation of the query selectivity to find the most efficient access plan. For queries referencing multiple attributes from the same relation, we need a multi-dimensional selectivity estimation technique when the attributes are dependent each other because the selectivity is determined by the joint data distribution of the attributes. Additionally, for multimedia databases, there are intrinsic requirements for the multi-dimensional selectivity estimation because feature vectors are stored in multi-dimensional indexing trees. In the 1-dimensional case, a histogram is practically the most preferable. In the multi-dimensional case, however, a histogram is not adequate because of high storage overhead and high error rates.
In this paper, we propose a novel approach for the multi-dimensional selectivity estimation. Compressed information from a large number of small-sized histogram buckets is maintained using the discrete cosine transform. This enables low error rates and low storage overheads even in high dimensions. In addition, this approach has the advantage of supporting dynamic data updates by eliminating the overhead for periodical reconstructions of the compressed information. Extensive experimental results show advantages of the proposed approach.
Random sampling techniques for space efficient online computation of order statistics of large datasets
Authors: Manku Gurmeet Singh , Rajagopalan Sridhar , Lindsay Bruce G. 
Published: November 2005

In a recent paper [MRL98], we had described a general framework for single pass approximate quantile finding algorithms. This framework included several known algorithms as special cases. We had identified a new algorithm, within the framework, which had a significantly smaller requirement for main memory than other known algorithms. In this paper, we address two issues left open in our earlier paper.
First, all known and space efficient algorithms for approximate quantile finding require advance knowledge of the length of the input sequence. Many important database applications employing quantiles cannot provide this information. In this paper, we present a novel non-uniform random sampling scheme and an extension of our framework. Together, they form the basis of a new algorithm which computes approximate quantiles without knowing the input sequence length.
Second, if the desired quantile is an extreme value (e.g., within the top 1% of the elements), the space requirements of currently known algorithms are overly pessimistic. We provide a simple algorithm which estimates extreme values using less space than required by the earlier more general technique for computing all quantiles. Our principal observation here is that random sampling is quantifiably better when estimating extreme values than is the case with the median.
Ripple joins for online aggregation
Authors: Haas Peter J. , Hellerstein Joseph M. 
Published: November 2005

We present a new family of join algorithms, called ripple joins, for online processing of multi-table aggregation queries in a relational database management system (DBMS). Such queries arise naturally in interactive exploratory decision-support applications.
Traditional offline join algorithms are designed to minimize the time to completion of the query. In contrast, ripple joins are designed to minimize the time until an acceptably precise estimate of the query result is available, as measured by the length of a confidence interval. Ripple joins are adaptive, adjusting their behavior during processing in accordance with the statistical properties of the data. Ripple joins also permit the user to dynamically trade off the two key performance factors of on-line aggregation: the time between successive updates of the running aggregate, and the amount by which the confidence-interval length decreases at each update. We show how ripple joins can be implemented in an existing DBMS using iterators, and we give an overview of the methods used to compute confidence intervals and to adaptively optimize the ripple join “aspect-ratio” parameters. In experiments with an initial implementation of our algorithms in the POSTGRES DBMS, the time required to produce reasonably precise online estimates was up to two orders of magnitude smaller than the time required for the best offline join algorithms to produce exact answers.
Data integration and warehousing in Telecom Italia
Authors: Trisolini Stefano M. , Lenzerini Maurizio , Nardi Daniele 
Published: November 2005

We discuss the main methodological and technological issues arosen in the last years in the development of the enterprise integrated database of Telecom Italia and, subsequently in the management of the primary data store for Telecom Italia data warehouse applications.
The two efforts, although driven by different needs and requirements can be regarded as a continous development of an integrated view of the enterprise data. We review the experience accumulated in the integration of over 50 internal databases, highlighting the benefits and drawbacks of this scenario for data warehousing and discuss the development of a large dedicated data store to support the analysis of data about customers and phone traffic.
DOMINO
Authors: Wolfson Ouri , Sistla Prasad , Xu Bo , Zhou Jutai , Chamberlain Sam 
Published: November 2005

Consider a database that represents information about moving objects and their location. For example, for a database representing the location of taxi-cabs a typical query may be: retrieve the free cabs that are currently within 1 mile of 33 N. Michigan Ave., Chicago (to pick-up a customer); or for a trucking company database a typical query may be: retrieve the trucks that are currently within 1 mile of truck ABT312 (which needs assistance); or for a database representing the current location of objects in a battlefield a typical query may be: retrieve the friendly helicopters that are in a given region, or, retrieve the friendly helicopters that are expected to enter the region within the next 10 minutes. The queries may originate from the moving objects, or from stationary users. We will refer to applications with the above characteristics as moving-objects-database (MOD) applications, and to queries as the ones mentioned above as MOD queries.
In the military MOD applications arise in the context of the digital battlefield (see [1]), and in the civilian industry they arise in transportation systems. For example, Omnitracs developed by Qualcomm (see[2]) is a commercial system used by the transportation industry, which enables MOD functionality. It provides location management by connecting vehicles (e.g. trucks), via satellites, to company databases. The vehicles are equipped with a Global Positioning System (GPS), and they automatically and periodically report their location.
Exploratory mining via constrained frequent set queries
Authors: Ng Raymond , Lakshmanan Laks V. S. , Han Jiawei , Mah Teresa 
Published: November 2005

Although there have been many studies on data mining, to date there have been few research prototypes or commercial systems supporting comprehensive query-driven mining, which encourages interactive exploration of the data. Our thesis is that constraint constructs and the optimization they induce play a pivotal role in mining queries, thus substantially enhancing the usefulness and performance of the mining system. This is based on the analogy of declarative query languages like SQL and query optimization which have made relational databases so successful. To this end, our proposed demo is not yet another data mining system, but of a new paradigm in data mining - mining with constraints, as the important first step towards supporting ad-hoc mining in DBMS.
In this demo, we will show a prototype exploratory mining system that implements constraint-based mining query optimization methods proposed in [5]. We will demonstrate how a user can interact with the system for exploratory data mining and how efficiently the system may execute optimized data mining queries. The prototype system will include all the constraint pushing techniques for mining association rules outlined in [5], and will include additional capabilities for mining other kinds of rules for which the computation of constrained frequent sets forms the core first step.
On-line reorganization in object databases
Authors: Lakhamraju Mohana K. , Rastogi Rajeev , Seshadri S. , Sudarshan S. 
Published: November 2005

Reorganization of objects in an object databases is an important component of several operations like compaction, clustering, and schema evolution. The high availability requirements (24 × 7 operation) of certain application domains requires reorganization to be performed on-line with minimal interference to concurrently executing transactions.
In this paper, we address the problem of on-line reorganization in object databases, where a set of objects have to be migrated from one location to another. Specifically, we consider the case where objects in the database may contain physical references to other objects. Relocating an object in this case involves finding the set of objects (parents) that refer to it, and modifying the references in each parent. We propose an algorithm called the Incremental Reorganization Algorithm (IRA) that achieves the above task with minimal interference to concurrently executing transactions. The IRA algorithm holds locks on at most two distinct objects at any point of time. We have implemented IRA on Brahma, a storage manager developed at IIT Bombay, and conducted an extensive performance study. Our experiments reveal that IRA makes on-line reorganization feasible, with very little impact on the response times of concurrently executing transactions and on overall system throughput. We also describe how the IRA algorithm can handle system failures.
Towards self-tuning data placement in parallel database systems
Authors: Lee Mong Li , Kitsuregawa Masaru , Ooi Beng Chin , Tan Kian-Lee , Mondal Anirban 
Published: November 2005

Parallel database systems are increasingly being deployed to support the performance demands of end-users. While declustering data across multiple nodes facilitates parallelism, initial data placement may not be optimal due to skewed workloads and changing access patterns. To prevent performance degradation, the placement of data must be reorganized, and this must be done on-line to minimize disruption to the system.
In this paper, we consider a dynamic self-tuning approach to reorganization in a shared nothing system. We introduce a new index-based method that faciliates fast and efficient migration of data. Our solution incorporates a globally height-balanced structure and load tracking at different levels of granularity. We conducted an extensive performance study, and implemented the methods on the Fujitsu AP3000 machine. Both the simulation and empirical results demonstratic that our proposed method is indeed scalable and effective in correcting any deterioration in system throughput.
Efficient and extensible algorithms for multi query optimization
Authors: Roy Prasan , Seshadri S. , Sudarshan S. , Bhobe Siddhesh 
Published: November 2005

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.
Counting, enumerating, and sampling of execution plans in a cost-based query optimizer
Authors: Waas Florian , Galindo-Legaria César 
Published: November 2005

Testing an SQL database system by running large sets of deterministic or stochastic SQL statements is common practice in commercial database development. However, code defects often remain undetected as the query optimizer's choice of an execution plan is not only depending on the query but strongly influenced by a large number of parameters describing the database and the hardware environment. Modifying these parameters in order to steer the optimizer to select other plans is difficult since this means anticipating often complex search strategies implemented in the optimizer.
In this paper we devise algorithms for counting, exhaustive generation, and uniform sampling of plans from the complete search space. Our techniques allow extensive validation of both generation of alternatives, and execution algorithms with plans other than the optimized one—if two candidate plans fail to produce the same results, then either the optimizer considered an invalid plan, or the execution code is faulty. When the space of alternatives becomes too large for exhaustive testing, which can occur even with a handful of joins, uniform random sampling provides a mechanism for unbiased testing.
The technique is implemented in Microsoft's SQL Server, where it is an integral part of the validation and testing process.
Expressing business rules
Authors: Ross Ronald G. 
Published: November 2005

Business rules are formal statements about the data and processes of an enterprise. My overall approach to business rules is described in [1, 2]. Here, I will briefly discuss things we have learned about the expression of business rules in the last several years. This will shed light on where we stand in understanding business rules today.
First, it is clearly important to separate analysis-level expression of business rules from their design-level expression. Most of what I will say here is aimed toward the design level, but let me start with the analysis level.
Effective expression of business rules at the analysis level requires formative guidelines or Business Rule Statement Templates. Such language templates are now offered commercially (by my company and others). Think of these language templates as text or sentence patterns, to ensure higher clarity and consistency. These templates are important for making the business rule approach practical.
At the design level, how business rules are expressed to users must be cleanly separated from how they are represented inside the system. What is good for one is not good for the other.
Index research (panel session)
Authors: Hellerstein Joseph M. , Kriegel Hans-Peter , Comet David , Falsutsos Christos , Ramakrishnan Raghu , Brown Paul 
Published: November 2005

Indexes and access methods have been a staple of database research — and indeed of computer science in general — for decades. A glance at the contents of this year's SIGMOD and PODS proceedings shows another bumper crop of indexing papers.
Given the hundreds of indexing papers published in the database literature, a pause for reflection seems in order. From a scientific perspective, it is natural to ask why definitive indexing solutions have eluded us for so many years. What is the grand challenge in indexing? What basic complexities or intricacies underlie this large body of work? What would constitute a successful completion of this research agenda, and what steps will best move us in that direction? Or is it the case that the problem space branches in so many ways that we should expect to continuously need to solve variants of the indexing problem?
From the practitioner's perspective, the proliferation of indexing solutions in the literature may be more confusing than helpful. Comprehensively evaluating the research to date is a near-impossible task. An evaluation has to include both functionality (applicability to the practitioner's problem, integration with other data management services like buffer management, query processing and transactions) as well as performance for the practitioner's workloads. Unfortunately, there are no standard benchmarks for advanced indexing problems, and there has been relatively little work on methodologies for index experimentation and customization. How should the research community promote technology transfer in this area? Are the new extensibility interfaces in object-relational DBMSs conducive to this effort?
Data access (tutorial session)
Authors: Blakeley José A. , Deshpande Anand 
Published: November 2005

With an explosion of data on the web, consistent data access to diverse data sources has become a challenging task. In this tutorial will present topics of interest to database researchers and developers building: interoperable middle-ware, gateways, distributed heterogeneous query processors, federated databases, data source wrappers, mediators, and DBMS extensions.
All of these require access to diverse information through common data access abstractions, powerful APIs, and common data exchange formats. With the emergence of the web, database applications are being run over the intranet and the extranet. This tutorial presents an overview of existing and emerging data access technologies. We will concentrate on some of the technical challenges that have to be addressed to enable uniform data access across various platforms and some of the issues that went into the design of these data access strategies.
AJAX
Authors: Galhardas Helena , Florescu Daniela , Shasha Dennis , Simon Eric 
Published: November 2005

@@@@
groups together matching pairs with a high similarity value by applying a given grouping criteria (e.g. by transitive closure). Finally,
ging
collapses each individual cluster into a tuple of the resulting data source. AJAX provides @@@@ for specifying data cleaning programs, which consists of SQL statements enriched with a set of specific primitives to express these transformations.

AJAX also @@@@. It allows the user to interact with an executing data cleaning program to handle exceptional cases and to inspect intermediate results. Finally, AJAX provides @@@@ @@@@ that permits users to determine the source and processing of data for debugging purposes.
We will present the AJAX system applied to two real world problems: the consolidation of a telecommunication database, and the conversion of a dirty database of bibliographic references into a set of clean, normalized, and redundancy free relational tables maintaining the same data.