Ceri, S., Pelagatti, G. (1988). Distributed Databases Principles & Systems. New York: McGraw-Hill.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....protocols for SDD 1 are discussed in [BRGP78] Sto79] describes the concurrency control protocol used in distributed INGRES. A locking protocol based on limited wait depth is proposed and evaluated in [FHRT93] A detailed discussion on the issues in the distributed database systems is given in [CP84] and [ OV91] A collection of some important papers on distributed concurrency control is presented in [Bha87] The performance studies of various distributed concurrency control algorithms have been reported in numerous papers, for example, MN82, CL88, CL89, CL91, FHRT93] 2.2.2 Distributed ....

Stefano Ceri and Giuseppe Pelagatti. Distributed Databases: Principles & Systems. McGraw-Hill, New York, 1984.

.... attempts to generalize the standard Bayesian database model, including horizontal independencies [6] 44] complex values [20] 44] and distributed Bayesian networks [7] 43] 47] parallel the development of horizontal dependencies [11] complex values [1] 18] and distributed databases [8] in the relational database model. More importantly, the implication problem for both models coincide with respect to two important classes of independencies, the BMVD class [13] used in the construction of Markov networks) and the conflict free sets [31] used in the construction of Bayesian ....

S. Ceri and G. Pelagatti, Distributed Databases: Principles & Systems. New York: McGraw-Hill, 1984.

....of the models and further details such as prices. Thus, the information agent has to decompose user queries to several queries suited for the given information providers. Then information has to be integrated again. Decomposing queries and integrating partial solutions involves several problems [CP85, DAT93] 1. Horizontal fragmentation: the relation r is distributed over several providers. For example, the relation of car companies and their models with corresponding attributes may be distributed over the car companies. 2. Vertical fragmentation: the attributes of r are distributed over ....

Stefano Ceri and Giuseppe Pelagatti. Distributed Databases - Principles & Systems. McGraw-Hill, 1985.

....user wants to keep its privacy (in the information theoretic sense) then essentially the only thing he can do is to ask for a copy of the whole database. Clearly, this is too much communication overhead, which makes it practically unacceptable. The rapid development of distributed databases (see [8]) and fast communication networks results in many scenarios in which the same database is replicated at several sites. This raises hope to get around the diculty of achieving privacy in the single server scenario. It may be possible to make queries to several servers such that from the answers the ....

S. Ceri and G. Pelagatti. Distributed Database Principles & Systems. McGraw Hill, 1984.

....bottom up approach) 2) or it can be designed by deriving the data from warehouse (called, top down approach) 13] In this paper, we advocate the top down approach as shown in Figure 1 because warehouse data can be fragmented based on the need of each data mart. In this approach, fragmentation [5, 20, 1] can play an important role, by fragmenting the DW into number of fragments that can be used as a data allocation unit to data marts. These fragments can be allocated to data marts so that most of the queries posed on a given data mart can be executed locally, thus communication cost can be ....

....fragments, each of the fragments contains a subset of R s attributes as well the primary key of R. ii) Horizontal partitioning (HP) partitions a relation R along its tuples. Each horizontal fragment (HF) has a subset of the tuples of the relation R. Two versions of HP are cited by the researchers [5]: primary HP and derived HP. Primary HP of a relation is performed using predicates that are defined on that relation. On the other hand, derived HP is the partitioning of a relation that results from predicates defined on another relation. A lot of work has been done on the partitioning in the ....

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S. Ceri and G. Pelagatti. Distributed Databases: Principles & Systems. McGraw-Hill International Editions, 1984.

....HP because it is well adapted to data warehouses. Also, the TPC D benchmark [60] in its implementation allows the utilization of HP, but it discards the utilization of the vertical partitioning. 3. 3 Horizontal Partitioning Algorithms in Databases Several algorithms were proposed in performing HP [17, 54, 56, 55, 59, 15, 27, 26]. These algorithms can be classified into two main categories: 1) query driven algorithms and (2) datadriven algorithms. 3.3.1 Query Driven Algorithms These algorithms are performed based on a set of most frequently asked queries and their access frequencies for a specific application [55] ....

....80 20 rule which considers that 20 of user queries account for 80 of the total data access in the database system. This category of algorithms is divided into two types: ffl Affinity based algorithms: The most proposed algorithms for HP in the relational and object databases are affinity based [17, 59, 56, 15, 27]. The affinities are used between predicates. Predicates having a high affinity are grouped together to form a horizontal fragment. The algorithm of Navathe et al. 56] starts by performing an analysis on the predicates defined by a set of queries accessing a relation to be horizontally ....

S. Ceri and G. Pelagatti. Distributed Databases: Principles & Systems. McGraw-Hill International Editions, 1984.

....a logical design technique that has been applied on relational databases. In our study, we will show how partitioning can be incorporated in DWs. 2. 1 Applicability of Partitioning Partitioning aims at reducing the number of disk IOs for query execution by minimizing accesses to irrelevant data [4]. We distinguish two types of partitioning: vertical and horizontal. Vertical partitioning of a relation R produces vertical fragments, each of which contains a subset of R s attributes as well as the primary key of R. Horizontal partitioning (HP) partitions a relation R along its tuples. Each ....

....the query processing can still be very high. HP aims at reducing irrelevant data accesses [1, 8] ffl Since OLAP queries use joins of multiple dimension tables and a fact table, the derived HP developed for relational databases can be used to efficiently process joins across multiple relations [4]. Two versions of HP are cited by researchers [3] primary HP and derived HP. Primary HP of a relation is performed using predicates that are defined on that relation. On the other hand, derived HP is the partitioning of a relation that results from predicates defined on another relation. 2.1.1 ....

S. Ceri and G. Pelagatti. Distributed Databases: Principles & Systems. McGraw-Hill International Editions, 1984.

....commits, CRep Equivalence constraint is violated again. Now, the xB is the consistent copy and the LAU 2;A is on the TBS list of xA . The respective DD graph is shown as the third DD graph in Figure 3. The CRep data item marked by the consistency marker is an analog to the primary copy update [8] that deals with data replication in distributed databases. However, in our DPC algorithm, every CRep data item can be the primary copy and the location of the primary copy is dynamically changed. As the TBSTrxn, LAU 1;B , is executed virtually by copying the effect of its corresponding LAU (LAU ....

S. Ceri and G. Pelagatti. Distributed Databases Principles & Systems. McGraw-Hill, New York, 1984.

....of the stock he is interested in. However, it is not difficult to prove that if the user wants to keep its privacy then essentially the only thing he can do is to ask for a copy of the whole database; clearly, this is an undesirable solution. The rapid development of distributed databases (see [8]) and all kind of data services ( information highways ) results in many scenarios in which the same database is replicated in several sites. This raises hope to get around the difficulty of achieving privacy in the single database scenario; it may be possible to make queries to several databases ....

S. CERI AND G. PELAGATTI. Distributed Database Principles & Systems. McGraw Hill, 1984.

....Each site hosts a local (centralized) DBMS which stores a portion of the database. The local DBMSs co operate with each other making the real distribution of the data invisible to the user. An operation involving data that is not available locally gives rise to a distributed transaction [BHN87, CP85, GR94] constituted by a set of sub transactions whose execution is performed concurrently on different sites of the network. Synchronization mechanisms, recovery techniques and concurrency control mechanisms must be implementsed in order to ensure a coherent transaction completion. The DDBMS ....

....data, and communicate with TM for informing about the success or failure of each sub transaction. TM has a global vision of the state of the transactions, thus can act as the commitment controller, deciding of a transaction success or failure. This architecture is quite standard (see for example [CP85] in the following section we ll describe our approach for developing a prototype of the architecture. 4 The prototype development: CaseLP and CLP In this section we describe a prototype of the architecture depicted in Section 3. The prototype has been implemented using CaseLP as the framework ....

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S. Ceri and G. Pelagatti. DISTRIBUTED DATABASES Principles & Systems. Computer Science Series. McGraw-Hill Intenational edition, 1985.

....cost functions use the description of the relations, e.g. cardinality or selectivity, to compute the cost. Observe that the operands for these functions may be intermediate relations, whose descriptions must be computed. We model the descriptor of a relation using the framework presented in [CP84], where it is called a profile. Such a descriptor encodes all the information about the relation that is needed for the cost functions. The descriptor of an n ary relation is a tuple containing (b i , c i , i = 0, 1, n) where b i and c i are the bag (the cardinality without removing ....

Ceri, S. and G. Pelagatti. Distributed Databases: Principles & Systems, McGrawHill Book Company, 1984.

....difficult to describe with current COOLs. Examples of such problems are deadlock free systems, controlling concurrent accesses to shared data, atomic execution of concurrent activities, termination detection of distributed processes, check pointing, and rollback. To integrate transaction semantics [3, 2] with concurrent and asynchronous nature of COOLs, HARMONY 2 incorporates a new transaction model, which we call concurrent transaction model. Concurrent transaction model is an extension of traditional nested transaction model that allows each transaction to involve concurrency, however still ....

....and so on. Concurrent transaction mechanism makes a subcomputation specified by a transaction block to be executed as an atomic transaction in the traditional sense: concurrent transaction mechanism guarantees three properties of the transaction, namely atomicity, isolation, and serializability [3, 2]. Atomicity is an all or nothing semantics: a transaction either succeeds completely or fails and leaves no effect. Success of a transaction is called commitment and failure is called abortion. In the above example, the three messages are either processed completely in case of commitment or ....

S. Ceri and G. Pelagatti. Distributed Databases: Principles & Systems. McGrawHill, New York, 1985.

....cost functions use the description of the relations, e.g. cardinality or selectivity, to compute the cost. Observe that the operands for these functions may be intermediate relations, whose descriptions must be computed. We model the descriptor of a relation using the framework presented in [CP84], where it is called a profile. Such a descriptor encodes all the information about the relation that is needed for the cost functions. The descriptor of an n ary relation is a tuple containing (b i ; c i ; i = 0; 1; n) where b i and c i are the bag (the cardinality without removing ....

Ceri, S. and G. Pelagatti. Distributed Databases: Principles & Systems, McGrawHill Book Company, 1984.

....prove (see Appendix A.1) that if the user wants to keep its privacy then essentially the only thing he can do is to ask for a copy of the whole database. Clearly, this is too much communication overhead, which makes it practically unacceptable. The rapid development of distributed databases (see [8]) and all kind of data services ( information highways ) results in many scenarios in which the same database is replicated in several sites. This raises hope to get around the difficulty of achieving privacy in the single database scenario. It may be possible to make queries to several databases ....

S. Ceri and G. Pelagatti. Distributed Database Principles & Systems. McGraw Hill, 1984.

No context found.

Ceri, S., Pelagatti, G. (1988). Distributed Databases Principles & Systems. New York: McGraw-Hill.

No context found.

Stefano Ceri and Guiseppe Pelagatti. Distributed Databases: Principles & Systems. McGraw-Hill, 1984.

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CP84 Ceri, S. and Pelagatti, G., Distributed Databases - Principles & Systems. McGraw-Hill, New York, N.Y., 1984.

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