W. Lipski, Jr. On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems, 4(3):262--296, September 1979.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... hug join project and select, and various dependencies are continuous functions in the lattice of generated relations It0] instance, although we do not know the specific color of an object, we may know that it is either red or blue. An approach to this kind of information has been described in [16]. More generally, a probability distribution for an unknown value within a domain may be either given or computable from the current database. A systematic approach to this problem is described in [24[ Moreover, additional information may be available on a specific instance of a null (e.g. ....

W. LIPSKI, On semantic issues connected with incomplete information databases, ACM Trans. Database Systems 4 (3)(1979), 262-296.

....any data model. Such a data value can be interpreted as the value at present unknown . The inclusion of missing information provides unknown data for the attributes in a database. As the relational database model has matured, researchers have examined the question of how to handle missing data [2,4,5,6,8,9,14]. The extensions of relational algebra have been addressed in [4,8,9] Such a broadened capability allows the user to probe the database for potential data relationships that might be useful but can not be retrieved by true relational algebra. Maybe operations, especially maybe join, have drastic ....

....base to filter out the data which are not of any importance to the user. 2.1 Null Values and Maybe Algebra In a partial database, exclusion of tuples witht missing data values may cause a loss of useful information. Issues of how to handle null values have been studied in the literature [5,6,8,9,14]. This study is; based on Codd s maybe algebra [4] which adopts a three . valued logic for handling partial relations. Maybe algebra extends the power of relational algebra operations in capturing more possible relationships among the data values. Naturally, as expected, this extension is at the ....

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Lipski, W., "On Semantic Issues Connected with Incomplete Information Databases," ACM Transactions on Database Systems, Vol. 4, No. 3, 1979, pp. 262-296.

....the users specification. We provide a query evaluation based on approximation for dealing with empty answers in our query mechanism. Approximative answers have been studied by many researchers in the past. Approximation is necessary usually when a database system has only partial information [12], 16] about the real world. In [11] several constructs have been defined for various kinds of partial information. In [2] it is argued that even when two systems have complete information about two separate worlds, the information that they possess may be insu#cient to answer certain queries. ....

W. Lipski. "On Semantic Issues Connected with Incomplete Information Databases". ACM Trans. Database Systems, 4(3):262--296, September 1979.

....support) and l TG (lower support) are indexes that refer to the minimum and maximum granule of the granularity TG within which an indeterminate instant is located. The event might have occurred after l TG and definitely occurred by u TG ; l TG and u TG , thus, correspond to the Lipski bounds [43]. Similarly, the function cast(g, TH) scales the instant and then returns the index corresponding to the first granule. Thus, the cast operation returns a determinate instant when applied to a determinate instant. For example, scale(2000 year , month) 2000 01 month 2000 12 month and cast(2000 ....

J. Lipski, On Semantic Issues Connected with Incomplete Information Databases,ACM Transactions of Database Systems, Vol. 4, No. 3, pp. 262-296, 1979.

....conclusion that missing values of object attributes can be handled in the way that null values are handled in relational databases, although the added complication introduced by inheritance must also be taken into account. Related to this work on missing attribute values are the e orts of Lipski [84] who concentrated upon the problem of incompletely speci ed attribute values, and contrasted the mismatch between the common occurrence of incomplete information and the fact that at the time no commercial databases provided support for incomplete information. Lipski was concerned with the problem ....

....Bill s phone number is one of 909281, 904131, or 909591. This is handled by allowing attributes to have sets of possible values, and queries are answered by supplying possible and known values in a way reminiscent of IEEE Transactions on Knowledge and Data Engineering, 8(3) 353 372. 12 Lipski [84]. The most interesting thing about this approach is that Morrissey then proceeds to quantify the uncertainty that this imprecision introduces by using techniques from information theory, e ectively hybridising Lipski s work with more numerical methods. He provides two measures for the uncertainty ....

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Lipski, W. (1979) On semantic issues connected with incomplete information databases, ACM Transactions on Database Systems, 4, 262-296.

....of the axioms describing a program in a xed part for the programming language and a so to say variable part for a concrete program can be model theoretically handled by use of modal logic. There, the xed axioms would be modeled by necessary axioms. But with exception of database theory, cf. Lip79, Lip81] we are not aware of a modal approach to programming languages which uses this framework for software maintenance or the other possible applications mentioned above. The view of programs as deductive systems introduced by Halln as and Schroeder Heister [HSH90, SH91] and also adopted by ....

W. Lipski. On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems, 4(3):262-296, 1979.

.... only at the end of a fault diagnosis we may know exactly which part of some machine was faulty but as long as we are searching, di#erent possibilities exist; Biological inheritance: if the parents have blood groups A and 0, the child must also have one of these two blood groups (example from [Lip79]) Natural language understanding: here there are many possibilities for ambiguity and they are represented most naturally by multiple intended models; Reasoning about concurrent processes: since we do not know the exact sequence in which certain operations are performed, again multiple ....

W. Lipski, Jr. On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems, 4:262--296, 1979.

....information. 1 Supported in part by NSF Grant IRI 90 04137 and AT T Doctoral Fellowship. 1 Introduction The idea of using approximate answers to queries against databases with partial information has been known in the database literature for more than ten years. In his classical papers, Lipski [25, 26] suggested to use two approximations to answer queries Q for which a precise answer can not be found. The lower approximation to the answer to Q consists of those objects for which one can conclude with certainty that they belong to the answer to Q. The upper approximation to the answer to Q ....

W. Lipski, On semantic issues connected with incomplete information in databases, ACM Trans. Database Systems 4 (1979), 262--296.

....sets, i.e. a set of values plus their probability of being the missing one. A survey of the work related to fuzzy sets and further references can be found in [DPY93] while the usage of fuzzy sets for the processing of uncertain queries is collected by Motro in a comparing article [Mot90] Lipski [Lip79] distinguishes the internal and the external interpretation of a query. The external one corresponds to the answer given if the real world would be completely known. The internal one in turn is the one given if there is some information missing. Missing information always concerns atomic attribute ....

W. Lipski. On semantic issues connected with incomplete information databases. ACM Transactions On Database Systems, 4(3):262--296, 1979.

....logic system. However, most real world information and knowledge are not presented as the precise values, rather they are often presented as the missing, incomplete, uncertain values. Whereas such incomplete information existed in all types of information systems and has been studied extensively [3,5,9,10,16,18,24], this paper concentrates on the problems existing in deductive database systems. A deductive database is a database containing both explicit and implicit facts. It provides the facilities to define implicit data in terms of general rules and the deductive ability to reason with the database ....

....a degree of basic indefinite information in a deductive database while adding only a little to the overall system complexity One approach would be to adopt the way of handling incomplete information in relational databases. Lipski proposed a mathematical model of an incomplete information system [9,10]. If the concept of incomplete information in relational databases can be introduced into a deductive database, the problem of a special indefiniteness can be confined to the problem of value incompleteness. In other words, instead of introducing the general form of disjunction into a deductive ....

[Article contains additional citation context not shown here]

W. Lipski, "On semantic issues connected with incomplete information databases", ACM Trans. on Database Systems, vol(4), 3, pp262-296, Sept. 1979.

.... only at the end of a fault diagnosis we may know exactly which part of some machine was faulty but as long as we are searching, different possibilities exist; ffl Biological inheritance: if the parents have blood groups A and 0, the child must also have one of these two blood groups (example from [21]) ffl Natural language understanding: here there are many possibilities for ambiguity and they are represented most naturally by multiple intended models; Preliminary results of this paper appeared in the Proceedings of the Sixth International Conference on Principles of Knowledge ....

W. Lipski, Jr. On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems, 4:262--296, 1979.

....information, but rather is a refinement of that information. They also address the problem of now in predictive updates; an indeterminate interval is a valid interval no matter when it was stored in the database. There are two bounds on the information represented by an indeterminate interval [Lipski 1979]. The first bound is the definite information. The definite information represents all that is definitely known about the interval and is the intersection of all of the possible intervals. The second bound 16 is the possible information. The possible information represents the maximum possible ....

W. Lipski, Jr. On Semantic Issues Connected with Incomplete Information Databases. ACM Transactions on Database Systems, 4(3):262--296, September 1979.

.... only at the end of a fault diagnosis we know exactly which part of some machine was faulty but as long as we are searching, different possibilities exist; ffl Biological inheritance: if the parents have blood groups A and 0, the child must also have one of these two blood groups (example from [Lip79]) ffl Natural language understanding: here there are many possibilities for ambiguity and they are represented most naturally by multiple intended models; ffl Conflicts in multiple inheritance: if we want to keep as much information as possible, we should assume disjunction of the inherited ....

W. Lipski, Jr. On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems, 4:262--296, 1979.

....no probabilities are used. Since there are no probabilities, the user in general is limited to querying for answers which are either definite or those which are possible (or combinations thereof) Historically, these alternatives have a well defined meaning in incomplete information databases [Lipski 1979]. Generalized valid times are composed of valid times by the operators of alternation (only one valid time applies) and or union (both valid times could apply) Alternation is used to model alternative temporal scenarios. We provide no capability for generalizing valid times to handle such ....

Lipski, W., Jr. "On Semantic Issues Connected with Incomplete Information Databases." ACM Transactions on Database Systems, 4, No. 3, Sep. 1979, pp. 262--296.

....of that information. They also ON THE SEMANTICS OF NOW IN DATABASES 137 address the problem of now in predictive updates; an indeterminate interval is a valid interval no matter when it was stored in the database. There are two bounds on the information represented by an indeterminate interval [36]. The first bound is the definite information. The definite information represents all that is definitely known about the interval and is the intersection of all of the possible intervals. The second bound is the possible information. The possible information represents the maximum possible extent ....

W. Lipski, Jr. On Semantic Issues Connected with Incomplete Information Databases. ACM Transactions on Database Systems, 4(3):262--296, September 1979.

....123456789 27 Ann 564738291 25 merge Name SS# Age John 123456789 h24; 27i Mary 987654321 32 Ann 564738291 25 Fig. 3. Example of or sets arising in merging databases Disjunctive information The idea of using disjunctive information as a means to express partiality was already present in [Lip79, Lip81] But it was not until almost ten years later that the first attempt was made to introduce disjunctions explicitly into the standard relational model. Consider the following example. Suppose we have two databases, D 1 and D 2 shown in figure 3. Assume that we merge D 1 and D 2 . It is ....

W. Lipski. On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems, 4(3):262--296, September 1979.

....We also discuss the semantics of approximation constructs and relationship between them. 1 Introduction The idea of using approximate answers to queries against databases with partial information has been known in the database literature for more than ten years. In his classical papers, Lipski [27, 28] suggests the use of two approximations to answer queries Q for which a precise answer cannot be found. The lower approximation to Q consists of those objects for which one can conclude with certainty that they belong to the answer to Q. The upper approximation to Q consists of those objects for ....

....promote to the relations Employees and CS1 gives us the new lower approximation that consists of one record [John, 15K, 76] Thus, it tells us that John from office 76 is a TA with salary 15K, and Mary with salary 12K could be a TA. Hence the result is an approximation in the sense of Lipski [27, 28]: we have the set of for sure answers and the set of maybe answers. 6 Conclusion Previous papers on approximate answers to queries against independent databases ( 5, 13, 29, 31] do not address two important problems, which are required for a general theory. First, we need a classification of ....

W. Lipski, On semantic issues connected with incomplete information in databases, ACM Trans. Database Systems 4 (1979), 262--296.

.... only at the end of a fault diagnosis we may know exactly which part of some machine was faulty but as long as we are searching, di#erent possibilities exist; Biological inheritance: if the parents have blood groups A and 0, the child must also have one of these two blood groups (example from [42]) Natural language understanding: here there are many possibilities for ambiguity and they are represented most naturally by multiple intended models; 2 . Reasoning about concurrent processes: since we do not know the exact sequence in which certain operations are performed, again multiple ....

W. Lipski, Jr. On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems, 4:262--296, 1979.

....ordering, i.e. eq(d,d 0 ) d v d 0 and d 0 v d This approach also provides a uniform treatment of null values [Zan84, Bis81] which are used in databases that represent incomplete information. Join and projection extend smoothly to data containing null values. However care must be taken [Lip79, IL84] to ensure that use of the algebra with these extended operations provides the semantics intended by the programmer. To represent null values, we also extend the syntax of Machiavelli terms with: null(b) the null value of a base type b the (polymorphic) null value of variant types ....

W. Lipski. On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems, 4(3):262--296, September 1979.

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W. Lipski, Jr. On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems, 4(3):262--296, September 1979.

No context found.

W. Lipski, Jr. On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems, 4(3):262--296, September 1979.

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W. Lipski. On semantic issues connected with incomplete information in relational databases. ACM Transactions on Database Systems, 4:262-296, 1979.

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J. Lipski, "On Semantic Issues Connected with Incomplete Information Databases," ACM Transactions of Database Systems, Vol. 4, No. 3, pp. 262-296, 1979.

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W. Lipski, Jr. On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems, 4(3):262--296, September 1979.

No context found.

W. Lipski, Jr. On semantic issues connected with incomplete information databases. ACM Transactions on Database Systems, 4(3):262--296, September 1979.

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