Minimum isolated failure immune networks are shown to be 2-trees. Further, subgraphs of 2-trees are shown to be exactly those graphs which contain no subgraph homeomorphic to the four-vertex complete graph. Together, these two characterizations yield a linear time algorithm for adding lines to a network to produce a minimum isolated failure immune network, whenever this is possible. This same algorithm, in conjunction with a linear time Steiner tree algorithm for 2-trees, yields a linear time Steiner tree algorithm for partial 2-trees. This contrasts with the known NP-completeness of the Steiner tree problem for planar graphs.

The query inference problem is to translate a sentence of a query language into an unambiguous representation of a query. A query is represented as an expression over a set of query trees. A metric is introduced for measuring the complexity of a query and also a proposal that a sentence be translated into the least complex query which "satisfies" the sentence. This method of query inference can be used to resolve ambiguous sentences and leads to easier formulation of sentences. l 349can be "executed" directly or translated into some logical expression (such as relational algebra or calculus). The query inference problem is uninteresting unless the QL provides some logical data independence. A measure of logical data independence can be achieved in a QL if tuple variables are implicitly bound to derived relations, rather than explicitly bound to specific relations. The relational QL without relations [15] is an example of such a language.Let A(t) be the set of attributes that appears with tuple variable t. We want a derived relation R(t) with a set of attributes A(t). There are several approaches to the computation of a derived relation (see [17, 201 for a general discussion). We review two universal relation approaches before introducing one of our own.One approach to the computation of R(t), given A(t), is to form a universal relation as the natural join of all the relations in the database and let R(t) be its projection onto A(t). (The relations can made to join completely by introducing tuples with null values.) This approach works well when the database scheme satisfies an acyclic join dependency [8].Maier and Ullman [16] demonstrate that when the database scheme is cyclic, the previous approach does not always assign an intuitive meaning to R(t). They explain how maximal objects can be used to give a more intuitive meaning to R(t). In their approach, an object is a computed relation representing a meaningful connection between all its attributes. A maximal object is an object contained in no other object. In the maximal object approach, R(t) is computed by selecting all maximal objects containing A(t), projecting them onto A(t), and taking the union. These maximal objects can be defined by the database administrator as in [17] or computed automatically using lossless joins [ 161.A problem with universal relational approaches is the unique role assumption among attributes, which can lead to a proliferation of attribute names [8]. This does not exist in the Entity-Relationship (ER) model because different roles between attributes are represented by different paths in the ER diagram. The maximal object approach has been adapted by Zhang and Mendelzon for the ER model [26]. They compute such objects automatically from the cardinalities of the relationships.In the maximal object approach, all maximal objects containing A(t) contribute to the computation of R(t) because there is no way to know which subset of maximal objects the user intended. We assume that a user wants only one object to be used in the computati...

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The problem of generating reasonable natural language-like responses to queries formulated in nonnavigational query languages with logical data independence is addressed. An extended ER model, the Entity-Relationship-Involvement model, is defined which assists in providing a greater degree of logical data independence and the generation of natural language explanations of a query processor's interpretation of a query. These are accomplished with the addition of the concept of an involvement to the model. Based on involvement definitions in a formally defined data definition language, DDL, an innovative strategy for generating explanations is outlined and exemplified. In the conclusion, possible extensions to the approach are given.

Technological advances have allowed natural resource information to become available in large databases over the Internet. We have developed a laboratory exercise that introduces students to geographic information systems (GIS) and soil and landuse databases available from the Natural Resource Conservation Service (NRCS), National Survey Database Access Facility. The exercise involves an introduction to spatial data using the State Soil Geographic (STATSGO) database and county soil surveys using a prototype survey of the Dade County area, Florida. In addition, students are introduced to the concept of scale by identifying land use problems utilizing online county soil survey data (1:12 000 to 1:63 360) and STATSGO data (1:250 000). Students had difficulty using the online soil survey due to the absence of map sheets and quickly identified the advantages of spatially referenced data available from STATSGO, which was viewable in map form using GIS software. This laboratory exercise introduced an application of GIS, spatial data available from the NRCS, and online soil surveys to introductory soil science students to solve a hypothetical regional planning problem. In addition to enhancing students' interests in GIS and associated technology, the exercise effectively demonstrated scale differences by comparison of information available from the STATSGO database and county soil surveys.