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For each nonclustered attribute, a broadcast cycle can be partitioned into a number of segments called metasegments [18], each of which holds a sequence of frames with nondecreasing (or nonincreasing) values of that attribute Thus, when we look at each individual metasegment, the data frames are clustered on that attribute and the indexing techniques discussed in the last subsection can still be applied to a metasegment The number of metasegments in the broadcast cycle for an attribute is called the scattering factor of the attribute The scattering factor of an attribute increases as the importance of the attribute decreases The index tree, signature, and hybrid methods are applicable to indexing multiattribute data frames [15] For multiattribute indexing, an index tree is built for each index attribute, and multiple attribute values are superimposed to generate signatures When two special types of queries, ie.

, queries with all conjunction operators and queries with all disjunction operators, are considered, empirical comparisons show that the index tree method, though performing well for single-attribute queries, results in poor access time performance [15] This is due to its large overheads for building a distributed index tree for each attribute indexed Moreover, the index tree method has an update constraint, ie, updates of a data frame are not reflected until the next broadcast cycle The comparisons revealed that the hybrid technique is the best choice for multiattribute queries due to its good access time and tune-in time The signature method performs close to the hybrid method for disjunction queries The index tree method has a similar tune-in time performance as the hybrid method for conjunction queries, whereas it is poor in terms of access time for any type of multiattribute queries 114 OTHER ISSUES 114.

1 Semantic Broadcast The indexing techniques discussed in Section 113 can help mobile clients filter information and improve tune-in time for data broadcast This type of broadcast is called itembased One major limitation of such item-based schemes is their lack of semantics associated with a broadcast Thus, it is hard for mobile clients to determine if their queries could be answered from the broadcast entirely, forcing them to contact the server for possibly additional items To remedy this, a semantic-based broadcast approach was suggested [20] This approach attaches a semantic description to each broadcast unit, called a chunk, which is a cluster of data items This allows clients to determine if a query can be answered based solely on the broadcast and to define precisely the remaining items in the form of a supplementary query Consider a stock information system containing stock pricing information.

The server broadcasts some data, along with their indexes A mobile client looking for an investment opportunity issues a query for the list of companies whose stock prices are between $30 and $70 (ie, 30 price 70) Employing a semantic-based broadcast scheme as shown in Figure 118, data items are grouped into chunks, each of which is indexed by a semantic descriptor A client locates the required data in the first two chunks since together they can satisfy the query predicate completely For the first chunk, it drops the item Oracle, 28 and keeps the item Dell, 44 For the second chunk, both items Intel, 63 and Sun, 64 are re-.

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1.4.2. Dynamic Formation of PC12 Organoids in RWV Bioreactors RWV bioreactors create a suitable environment for generating macroscopic aggregates, which, because of the unique properties of this system, can differentiate into functional, tissuelike constructs, or, as we call them, organoids [Lelkes and Unsworth, 2002]. The principles of RWV bioreactors are described elsewhere in detail [Begley and Kleis, 2000; Hammond and Hammond, 2001] In this part of our chapter we will describe methods to generate PC12 organoids with and without

The information architecture for a centralized database environment (CDBE) conforms to the American National Standards Institute (ANSI) standard proposed and approved by the Standard Planning and Requirements Committee (SPARC) [SPARC75] [SPARC76] [SPARC77] called the ANSI/SPARC three-level schema architecture as depicted in Figure 1.8. This architecture provides for three separate layers of abstraction the external, conceptual, and internal schemas. Users of such a system are assigned external schemas (also known as external views), which they then use to access the information in the underlying database. These views are created from the conceptual schema that encompasses the entire set of data in the database. The

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