P. Buneman, S. Davidson, K. Hart, C. Overton, L. Wong. A data transformation system for biological data sources. In VLDB'95, pages 158--169.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....to pinpoint. Documents are often only weakly structured using HTML tags, and the inner logical organization is user dependent. Thus, automatic extraction of, say, article titles, becomes a difficult task. Every developer of Web applications has to deal with, at least, some of the above issues [1, 2, 4, 5, 6, 7, 8, 9, 11, 18, 19]. A problem in many of the efforts undertaken in the past is that they adopt a complete system approach. Thus their solutions are often tailored to a specific application, rather than being general. Additionally, by being parts of complete systems, solutions cannot be used independently, or ....

P. Buneman, S. Davidson, K. Hart, C. Overton, and L. Wong. A data transformation system for biological data sources. In Proc. VLDB, 158--169, 1995.

....data sources. The Sequence Re trieval System (SRS) 43] initially developed at the European BioInformatics Institute and then further developed by Lion BioScience AG, provides a data management and query mechanism built around systematically warehousing databases as a federation. The Kleisli [20, 22, 42] system provides a system atic approach to managing and integrating external databases, and uses a functional query language to perform correlations across nested databases. Acedb [30] is an open source special purpose object based database designed to manage biological data. There are a number ....

P. BUNEMAN, S. B. DAVIDSON, K. HART, G. OVER- TON, L. WONG. A Data Transformation System for Biological Data Sources. Proceedings of the 21st International Conference on Very Large Databases (VLDB 1995.

....that plan over the Wrapped Sources to yield an answer to the query. Sources are wrapped so that they can be accessed in a syntactically consistent manner. In TAMBIS, each source is represented as a collection of function calls, which are evaluated by the Collection Programming Language (CPL) [6]. The sources currently used in TAMBIS are SWISS PROT, ENZYME, CATH, BLAST and PROSITE. Conceptual Query Formulation Interface Source Selection and Query Planning source independent conceptual query source dependent query plan results Ontology Server Sources and Services ....

....basis, using the resources available within many programming languages. CPL BioKleisli is one of the few systems to offer wrapper services together with a query language that is flexible enough to cope with bioinformatics resources. The output from the TAMBIS system is a query plan written in CPL [6] using a modified version of the BioKleisli library of biological database wrappers [9] CPL (Collection Programming Language) allows the concise expression of retrieval requests over collections of data, with data types for representing arbitrarily nested sets, bags, lists, records and variants. ....

P. Buneman, S.B. Davidson, K. Hart, C. Overton, and L. Wong. A Data Transformation System for Biological Data Sources. In Proc. 21st VLDB, pages 158--169. Morgan Kaufmann, 1995.

....The Collection Programming Language (CPL) is a functional programming language that allows data to be described and manipulated as complex data types such as sets, lists and records. These data types are suitable for modelling biological data and have been used to do so in the BioKliesli system (Buneman 1995) where biological sources are given a CPL driver and a set of functions to manipulate data. CPL BioKliesli thus acts as a multidatabase language and provides ways of manipulating and piping results, allowing the user to formulate complex, ad hoc queries. The details of location and access to these ....

Buneman P., Davidson S.B., Hart K., Overton C. and Wong L. A Data Transformation System for Biological Data Sources In Proceedings of VLDB, Sept. 1995 (Zurich, Switzerland).

....because fully extensible functionality is required for an environment that keeps changing not only in the size of the data but also in the schema. For example, bioinformatics applications require specialized data structures and customized tables for each new experimental technique introduced [Buneman et al. 1995]. Such frequent changes to source schema are not well addressed by current systems. Information Integration With the rapid emergence of data formats and applications such as bioinformatics supporting a veritable cottage industry of databases, information integration becomes paramount to support ....

....a veritable cottage industry of databases, information integration becomes paramount to support holistic scenarios. For example, the query Find all information pertaining to human chromosome 22 was considered an impossible query under the Human Genome Project until just a few years back [Buneman et al. 1995]. Information integration is typically addressed by remapping queries to originating sources, introducing a transparency layer of middleware in between data sources, or using other mediator based schemes. This is thus one of the main issues underlying applications such as health care management ....

Buneman, P., Davidson, S., Hart, K., Overton, C., and Wong, L. (1995). A Data Transformation System for Biological Data Sources. In Proceedings of the VLDB Conference.

....stored under different formats: relational or ODMG 1 model (in databases) SGML or LaTex (documents) DX formats (data exchange formats in scientific data) Step (CAD CAM data) etc. Their integration is a very active field of research and development (see for instance, for a very small sample, [13, 9, 10, 12, 11, 15, 25, 26]) In this paper, we provide a formal foundation to facilitate the integration of such heterogeneous data and the maintenance of heterogeneous replicated data. A sound solution for a data integration task requires a clean abstraction of the different formats in which data are stored, and means ....

P. Buneman, S. Davidson, K. Hart, C. Overton, and L. Wong. A data transformation system for biological data sources. In Proc. Int. Conf. on Very Large Data Bases (VLDB), pages 158--169, Zurich, Switzerland, 1995.

....has to ask the model what are the parents, children and siblings of the concept, as well as what relationships to which other concepts are valid. TAMBIS is one of several systems that attempt to allow queries to be formed over multiple information sources, such as SRS [3] OPM [4] and CPL [2]. The principal difference between TAMBIS and these systems is the level of transparency. TAMBIS is completely transparent, the user need only express their biological goal in order to form a query. TAMBIS uses the conceptual model to give the illusion of a common user interface and manage the ....

P. Buneman, S.B. Davidson, K. Hart, C. Overton, and L. Wong. A data transformation system for biological data sources. In Proceedings of VLDB, Sept 1995. 2

....and their phosphorylation site motifs . This query is answered by the three sources Swiss prot, Blast and Prosite. The user does not have to choose the sources, the key words with which to filter the proteins etc. Other systems, such as SRS [4] Bio Kleisli CPL (Collection Programming Language) [3] and OPM [5] have attempted to integrate multiple bioinformatics sources, but have done so less transparently. All have the integration and reconciliation of some aspects of heterogeneity in common. They all, however, still leave the user to choose sources, the attributes to be used and order of ....

P. Buneman, S.B. Davidson, K. Hart, C. Overton, and L. Wong. A data transformation system for biological data sources. In Proceedings of VLDB, Sept 1995.

....a global schema over a collection of sources that can be relational databases or structured files. OPM views can be used to hide individual sources, although, unlike TAMBIS, the global schema is described using an object data model, and the integration methodology is very much bottom up. CPL [5] comes with a library of functions that supports a syntactically consistent view of biological sources, plus a comprehension based query language for combining information derived from the sources. P FDM [12] combines database functionality with source specific entity types and functions that are ....

....in the query builder. This leads to a 3 list of the criteria defined on motif being displayed, from which the user can make selections. A screenshot of the final representation of the query is given in figure 2. 2.3. The Wrappers The output from the TAMBIS system is a query plan written in CPL [5]. CPL (Collection Programming Language) is a comprehension based language with data types for representing arbitrarily nested sets, bags, lists, records and variants. An example CPL query, which retrieves all motifs in guppy proteins, is as follows: m p get sp entry by os( guppy ) ....

[Article contains additional citation context not shown here]

P. Buneman, S. Davidson, K. Hart, C. Overton, and L. Wong. A Data Transformation System for Biological Data Sources. In Proc. 21st VLDB. Morgan Kaufmann, 1995.

....with it. It is the values of these molecular properties that determine which molecule is actually represented by the image on the screen. The properties specifiable are name, type, state and associated details, location, comments, and associated BioKRIS (Kleisli Related Integration Software) [3, 4] and other remote database queries. Each molecule is automatically provided with a unique name upon creation, which the user may alter as desired. Each molecule must also have a type specified. This type is based upon the underlying type database (see Section 3) and determines what the molecule ....

Buneman P., Davidson S.B., Hart K., Overton C., & Wong L., "A Data Transformation System for Biological Data Sources". In Proceedings of 21st International Conference on Very Large Data Bases, 158-169, Zurich, Switzerland. September 1995.

....or knowledge base formalism. This domain model or metadata schema captures the basic vocabulary used for the description of information in a domain. Information Manifold [13] PICSEL [56] SIMS [28] InfoQuilt 52 , Infomaster [55] HERMES [99] Garlic [25, 90, 59] AURORA [106] DIOM [76] and CPL [23]. An information resource is integrated in the system by providing a description of its structure (i.e. the structure descriptive metadata) in terms of the domain model. In this approach, the major task is to perform the schema integration. In open environments such as the Web, mediation systems ....

P. Buneman, S. B. Davidson, K. Hart, C. Overton, and L. Wong. A data transformation system for biological data sources. In Proceedings of International Conference on Very Large Databases (VLDB), pages 158--169, Zurich, Switzerland, September 1995.

....of what distinguish our focus on the constraint mapping problem from other integration efforts and how our approach can be applied in the common mediation architecture [1, 2] There has also been much work on data translation and schema integration. The main focus of these related efforts (e.g. [20, 21, 22, 23, 24, 25]) is to unify data representations across mismatched domains by transforming data to a unified context, where queries can be performed. In contrast, our complementary goal is to map queries to the native domain where data reside. We believe our approach is especially well suited for autonomous ....

P. Buneman, S. Davidson, K. Hart, C. Overton, and L. Wong. A data transformation system for biological data sources. In Proc. of the 21st VLDB Conf., Zurich, Switzerland, 1995.

....the planner and executes that plan over the Wrapped Sources to yield an answer to the query. Sources are wrapped so that they can be accessed in a syntactically consistent manner. In TAMBIS, each source is represented as a collection of function calls, which are evaluated by a language called CPL [5]. The sources currently used in TAMBIS are SWISSPROT, ENZYME, CATH, BLAST and Prosite. The remainder of this paper is organised as follows. Section 2 gives a brief overview of the TAMBIS ontology, describing its scope and the language in which it is implemented. Section 3 describes how users ....

....This service allows objects representing databank entries to be retrieved through the SRS query language. This should allow remote access to a large number of databanks and analysis programs, along with a rudimentary query facility. The output from the TAMBIS system is a query plan written in CPL [5] using a modified version of the BioKleisli library of biological database wrappers [8] CPL (Collection Programming Language) allows the concise expression of retrieval requests over collections of data, with data types for representing arbitrarily nested sets, bags, lists, records and variants. ....

P. Buneman, S.B. Davidson, K. Hart, C. Overton, and L. Wong. A Data Transformation System for Biological Data Sources. In Proc. 21st VLDB, pages 158--169. Morgan Kaufmann, 1995.

....interfaces is not well addressed in multidatabase systems, see Section 2. In this paper, we present a prototype system QUICK (QUery Interface to CPL Kleisli) CPL [3] Collection Programming Language) is a high level multidatabase language implemented on top of an open query system called Kleisli [2], which can handle nested relations and structured files. QUICK is built on top of this system and serves as its graphical query interface to multiple autonomous and heterogeneous databases. QUICK aims to minimize the effort of end users in formulating queries for multidatabase systems. QUICK is ....

....D.C. They also exist in a number of different formats. For example, GDB is a Sybase relational database and GenBank is a data source consisting of structured files in the ASN.1 (Abstract Syntax Notation) format. These are just two of the databases in the Human Genome Project. It was noted in [2] that structured files in various formats are adopted in preference over database management systems for the following reasons. Biological data are generally complex structures in its natural form. It can include sequential data as well as deeply nested record structures. It can also contain a ....

[Article contains additional citation context not shown here]

P. Buneman, et. al. A Data Transformation System for Biological Data Sources. VLDB'95, 158--169.

....a global schema over a collection of sources that can be relational databases or structured files. OPM views can be used to hide individual sources, although, unlike TAMBIS, the global schema is described using an object data model, and the integration methodology is very much bottom up. CPL [5] comes with a library of functions that supports a syntactically consistent view of biological sources, plus a comprehension based query language for combining information derived from the sources. P FDM [12] combines database functionality with source specific entity types and functions that are ....

....in the query builder. This leads to a 3 list of the criteria defined on motif being displayed, from which the user can make selections. A screenshot of the final representation of the query is given in figure 2. 2.3. The Wrappers The output from the TAMBIS system is a query plan written in CPL [5]. CPL (Collection Programming Language) is a comprehension based language with data types for representing arbitrarily nested sets, bags, lists, records and variants. An example CPL query, which retrieves all motifs in guppy proteins, is as follows: m p get sp entry by os( guppy ) ....

[Article contains additional citation context not shown here]

P. Buneman, S. Davidson, K. Hart, C. Overton, and L. Wong. A Data Transformation System for Biological Data Sources. In Proc. 21st VLDB. Morgan Kaufmann, 1995.

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P. Buneman, S. Davidson, K. Hart, C. Overton, L. Wong. A data transformation system for biological data sources. In VLDB'95, pages 158--169.

No context found.

Buneman, P., Davidson S.B., Hart, K., Overton, C., Wong, L. "A Data Transformation System for Biological Data Sources". Proceedings of the 21st VLDB Conference, Zurich, Switzerland, 1995

....filter promotion, and code motion. It optimizes scanning and printing of external files. It has been extended to deal with joins 6 by picking alternative join operators and by migrating them to external servers. More details can be found in The Kleisli Query System Reference Manual and in [26, 2]. 1.3 Acknowledgement I would like to thank Peter Buneman, Leonid Libkin, Val Tannen, and Dan Suciu. They greatly influenced the design of CPL. 7 2 The Core of CPL I first describe CPL s types. Then I describe the core fragment of CPL. The core fragment is based on the central idea of ....

....henceforth, except when giving typing rules. 2.3 Examples: CPL s Modeling Power Sets, lists, bags [11] records, and variants [7] are supported in CPL. These types can be freely combined, giving rise to a rich and flexible data model. Example. Here is a list of sets of numbers in CPL: [ 1, 2, 3, 4, 5, 6, 3, 7 ] ; Result : 1, 2, 3, 4, 5, 6, 3, 7] Type : num] Example. We can model the employee salary history example of Makinouchi [12] by a nested relation as below. #name: tom , #history: #date: june 1993 , #salary: 2000) #date: july 1993 , #salary: 2100) #name: jim , #history: ....

[Article contains additional citation context not shown here]

Peter Buneman, Susan Davidson, Kyle Hart, Chris Overton, and Limsoon Wong. A data transformation system for biological data sources. In Proceedings of 21st International Conference on Very Large Data Bases, pages 158--169, Zurich, Switzerland, August 1995.

....35 5.6 Rules Using Meta Information : 37 6 Conclusion 40 4 1 Introduction 1. 1 Introduction Kleisli is a system for querying heterogeneous data sources that are distributed over a wide area network and are not necessarily relational [5]. It is being used to provide on demand integration of a number of databases in the Human Genome Project [13] It is implemented in Concurrent ML [21] On top of Kleisli is a high level query language called CPL, for Collection Programming Language. Both CPL and much of the internal structure of ....

....In this section, we use an example query that joins data from two relational databases to illustrate some features of the Kleisli CPL system and some optimizations performed by our optimizer. Note that Kleisli CPL supports complex objects where sets, bags, and lists can be arbitrarily nested [5]. Furthermore, many data sources in our Human Genome Project environment are non relational. The use of the relational example below is to simplify explanation. Everything in this paper applies to non relational queries in our system. 2.1 Original Query The unoptimized CPL query below accesses ....

[Article contains additional citation context not shown here]

Peter Buneman, Susan Davidson, Kyle Hart, Chris Overton, and Limsoon Wong. A data transformation system for biological data sources. In Proceedings of 21st International Conference on Very Large Data Bases, pages 158--169, Zurich, Switzerland, August 1995.

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P. Buneman, S. Davidson, K. Hart, C. Overton, L. Wong. A Data Transformation System for Biological Data Sources. In Proceedings of the 21st VLDB Conference. 1995.

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P. Buneman, S.B. Davidson, K. Hart, C. Overton, and L. Wong. A data transformation system for biological data sources. In Proceedings of the 21st VLDB Conference, Zurich, Switzerland, 1995. VLDB Endowment, Saratoga, Calif.

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Buneman, P., Davidson, S.B., Hart, K., Overton, C., & Wong, L., eds. A Data Transformation System for Biological Data Sources. Proceedings of 21st International Conference on Very Large Data Bases. 1995: Zurich, Switzerland.

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P. Buneman, S. Davidson, K. Hart, C. Overton, and L. Wong. A data transformation system for biological data sources. In Proceedings of the 21st VLDB Conference, Zurich, Switzerland, 1995. VLDB Endowment, Saratoga, Calif.

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P. Buneman, S.B. Davidson, K. Hart, G.C. Overton, and L. Wong. A data transformation system for biological data sources. In Proc. of the Twenty-first International Conference on Very Large Data Bases, pages 158--169, Zurich, Switzerland, September 1995.

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P. Buneman, S. Davidson, K. Hart, C. Overton, and L. Wong. A data transformation system for biological data sources. In Proc. VLDB, 1995.

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