Amihai Motro. Vague: a user interface to relational databases that permits vague queries. ACM Trans. Inf. Syst., 6(3):187--214, 1988.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....the hierarchy of partitions. Although motivated by applications where there is a strict decomposition of the space, this work is useful for a variety of situations beyond GIS and statistical data analysis. In particular hierarchical domains can solve problems related to cooperative query answering [Mot86, Mot88, CCL91]. We give an example where the user can query a database with incomplete knowledge of the domain of the attributes present in the query: the user does not know at which abstraction level data is represented. Section 2 introduces the problem through examples. A model for multi scale representation ....

....addressed this issue in a more general context known as Cooperative Query Answering which deals with syntactically correct queries which provide a NULL answer. For example, the Type Abstraction Hierarchy of [CCL91] combines an hierarchy similar to ours with object oriented class hierarchies. [Mot86, Mot88] also allow some query generalization. 5 Implementation with O R SQL This model has already been partially implemented with the DBMS O [Rig95, PR95] We illustrate such an implementation by means of a few examples from Section 4. There are several ways of implementing a set of ordered covers. ....

A. Motro. VAGUE: a User Interface to Relational Databases that Permits Vague Queries. ACM Transactions on Office Information Systems, 6(3):187-- 214, 1988.

....like those [15, 21, 9] Based on the user feedback, the system computes new query parameters and executes the re ned query. Another way to re ne the query is for the user to explicitly modify the perception model, i.e. to explicitly change weights to better capture her perception of similarity [12, 5]. In either case, the user may continue re nement iterations until she is satis ed with the results. Recent work shows that query re nement techniques signi cantly improve the quality of answers over a few iterations [15, 9, 18, 21] While there has been a lot of research on improving the ....

....have been generalized to multimedia documents, e.g. image retrieval [18] uses image features to capture aspects of the image content, and adapts IR techniques to work on them. Techniques to incorporate similarity retrieval in databases have also been considered both for text and multimedia [12]. There are several algorithms to support top k similarity queries, such as Fagins [6] algorithm. There are a plethora of spatial join algorithms that perform better than the reliable nested loop join, 11] presents a good overview of the algorithms available to solve this problem. Koudas [11] ....

Amihai Motro. VAGUE: A user interface to relational databases that permits vague queries. ACM TOIS, 6(3):187-214, July 1988.

....houses for the user to inspect. If no houses match the query specification exactly, the system might return houses in Santa Ana (a city near Irvine) or two bedroom houses in Irvine or more expensive houses as the top matches to the query. An important aspect of top k queries is user subjectivity [13], 7] To return good quality answers, the system must understand the user s perception of similarity, i.e. the relative importance of the attributes features to the user. The system models user perception via the distance functions (e.g. Euclidean in the above example) and the weights ....

....the system will compute the new query objects and the new weights and execute the refined query. Another way to refine the query is that the user explicitly modifies the perception model, i.e. she explicitly changes the weights of the features so as to better capture her perception of similarity [13], 7] In either case, the user can continue refining the query over as many iterations she wants till she is satisfied with the results. Recent work shows that query refinement techniques significantly improve the quality of answers and answers improve with more iterations of feedback [23] 18] ....

[Article contains additional citation context not shown here]

A. Motro, "Vague: A User Interface to Relational Databases that Permits Vague Queries," ACM Trans. Office Information Systems, vol. 6, no. 3, July 1988.

....for widespread diffusion. Currently, the largest part of approaches to matchmaking are based on Business to Consumer (B2C) portals, which strive to reproduce, with limited success, the usual commercial interaction taking place in physical stores. The technology relies on vague query answering [8], with the aid of weights attributed to several search variables. Many B2C portals on the Internet still basically follow that approach, allowing the specification of given ranges for say, price, dates, etc. Other approaches recently implemented carry out a surrogate of matchmaking as an auction, ....

A. Motro. Vague: A user interface to relational databases that permits vague queries. ACM Trans. on Office Information Systems, 6(3):187--214, 1988.

....predicates. In the context of a similarity query, the following concepts are important: similarity score, similarity predicate, scoring rule, and ranked retrieval. Ranked retrieval stems from the intuitive desire of users of similarity based searching systems to see the best answers rst [15, 4], therefore the answers are sorted, or ranked on their overall score S. We now cover the remaining concepts in detail. A similarity based search in a database is an SQL query with both precise and similarity predicates. Precise predicates return only those data objects that exactly satisfy them, ....

....distance models. We use a string to pass the parameters as it can easily capture a variable number of numeric and textual values. The similarity score S output parameter is set during the matching. Similarity predicates of this form were used in other similarity searching systems such as VAGUE [15] and others [1] We further designate some similarity predicates as joinable: De nition 3 (Joinable similarity predicate) A similarity predicate is joinable i it is not dependent on the set of query values remaining the same during a query execution, and can accept a query values set with ....

[Article contains additional citation context not shown here]

Amihai Motro. VAGUE: A user interface to relational databases that permits vague queries. ACM TOIS, 6(3):187-214, July 1988.

....predicates. In the context of a similarity query, the following concepts are important: similarity score, similarity predicate, scoring rule, and ranked retrieval. Ranked retrieval stems from the intuitive desire of users of similarity based searching systems to see the best answers rst [30, 23, 4], therefore the answers are sorted, or ranked on their overall score S. We now cover the remaining concepts in detail. A similarity based search in a database is an SQL query with both precise and similarity predicates. Precise predicates return only those data objects that exactly satisfy them, ....

....capture a variable number of numeric and textual values. The function returns a Boolean value to facilitate integration with SQL. The similarity score S output parameter is set during the matching. Similarity predicates of this form were used in other similarity searching systems such as VAGUE [23] and others [1] An example de nition of a similarity predicate for a user de ned data type is: Example 2.2 (Geographic location similarity predicate) An example data type for two dimensional geographic locations (cf. example 1.1) is the type location(x: real, y: real) The similarity function ....

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Amihai Motro. VAGUE: A user interface to relational databases that permits vague queries. ACM Transactions on Oce Information Systems, 6(3):187-214, July 1988.

....the problem of retrieval by content from databases that contain multimedia presentations. We first introduce a model (i.e. an abstraction) of a multimedia presentation. Traditionally, retrieval by content from such databases concentrated on retrieval of images, and of video audio sequences ([11, 15, 27]) In contrast, our model captures the textual, spatial, and temporal attributes of the presentation. In our model, for example, a slide in the presentation is represented by a relational tuple containing the text of the slide, the time at which the slide starts to be displayed on the screen and ....

....based on a probabilistic model using relevance feedback. The main disadvantage there is that the query language is more restrictive due to independence assumptions. Multimedia Retrieval Our concept of approximate queries is similar to that of content based queries in multimedia applications [15]. Most recent work in this area focused on specific data types such as text, images, video audio, etc. There is a solid IR technology to retrieve documents based on their content ( 29, 30] and research in image processing led to systems such as QBIC [21] MARS [27] In contrast, we introduced a ....

Motro A. VAGUE: A user interface to relational databases that permits vague queries. ACM Transactions on Office Information Systems, 6(3), 1988.

....integration [2, 21] ffl Existing databases integration : this process gives a global scheme, which represents a set of several existing databases. It creates a virtual view in which all the different databases are brought together. Then the users see and work on one database, instead of several [10, 18, 20, 25, 15, 1]. ffl Design scheme integration : this process gives a global design scheme, from several schemes, each of them depicting the same reality according to different perception ways. Our work belongs to this second kind of integration. The first integration tools proposed where based on the ....

A. Motro. Vague : a user interface to relational databases that permits vague queries. ACM transactions on office information systems, 6(3):187--214, July 1988.

....of similarity queries, which are a major concern in multimedia environments. Indeed, it is a fact that similarity queries arise even if the database does not store imprecise information at all, provided similarity operators are de ned. This is also the scenario considered by the VAGUE system [16], where, however, important features are missing, such as weights, the Top operator, and fuzzy attributes. Further, problems related to query optimization are not considered in [16] Recent work by Adali et al. 1] addresses issues similar to ours, but important di erences exist. First, they do ....

....at all, provided similarity operators are de ned. This is also the scenario considered by the VAGUE system [16] where, however, important features are missing, such as weights, the Top operator, and fuzzy attributes. Further, problems related to query optimization are not considered in [16]. Recent work by Adali et al. 1] addresses issues similar to ours, but important di erences exist. First, they do not consider weights, that we have shown to introduce new interesting problems in the query optimization scenario. Second, they are mainly concentrated on problems related to the ....

A. Motro. VAGUE: A User Interface to Relational Databases that Permits Vague Queries. ACM Trans. on Oce Information Systems, 6(3):187-214, July 1988.

....of similarity queries, which are a major concern in multimedia environments. Indeed, it is a fact that similarity queries arise even if the database does not store imprecise information at all, provided similarity operators are defined. This is also the scenario considered by the VAGUE system [Mot88] where, however, important features are missing, such as weights, the Top operator, and fuzzy attributes. Further, problems related to query optimization are not considered in [Mot88] Recent work by Adali et al. ABSS98] addresses issues similar to ours, but important differences exist. First, ....

....at all, provided similarity operators are defined. This is also the scenario considered by the VAGUE system [Mot88] where, however, important features are missing, such as weights, the Top operator, and fuzzy attributes. Further, problems related to query optimization are not considered in [Mot88] Recent work by Adali et al. ABSS98] addresses issues similar to ours, but important differences exist. First, they do not consider weights, that we have shown to introduce new interesting problems in the query optimization scenario. Second, they are mainly concentrated on problems related to ....

A. Motro. VAGUE: A User Interface to Relational Databases that Permits Vague Queries. ACM Trans. on Office Information Systems, 6(3):187--214, Jul 1988.

....of similarity queries, which are a major concern in multimedia environments. Indeed, it is a fact that similarity queries arise even if the database does not store imprecise information at all, provided similarity operators are defined. This is also the scenario considered by the VAGUE system [16], where, however, important features are missing, such as weights, the Top operator, and fuzzy attributes. Further, problems related to query optimization are not considered in [16] Recent work by Adali et al. 1] addresses issues similar to ours, but important differences exist. First, they do ....

....at all, provided similarity operators are defined. This is also the scenario considered by the VAGUE system [16] where, however, important features are missing, such as weights, the Top operator, and fuzzy attributes. Further, problems related to query optimization are not considered in [16]. Recent work by Adali et al. 1] addresses issues similar to ours, but important differences exist. First, they do not consider weights, that we have shown to introduce new interesting problems in the query optimization scenario. Second, they are mainly concentrated on problems related to the ....

A. Motro. VAGUE: A User Interface to Relational Databases that Permits Vague Queries. ACM Trans. on Office Information Systems, 6(3):187--214, July 1988.

....database systems and IR technology. None of the current work in fuzzy databases provides any means for query refinement. For crisp databases, there are two approaches in current literature which support vague queries. None of them is based on fuzzy logic. First, the VAGUE system introduced in [16] is based on a variant of the vector space model for database relations. The distance between two tuples is computed by aggregating the individual distances between the corresponding attributes by means of a weighted Euclidean distance measure. In contrast, our aggregation function is is given by ....

Motro A. "VAGUE: A user interface to relational databases that permits vague queries" ACM Transactions on Office Information Systems, 6(3), 1988.

....houses for the user to inspect. If no houses match the query specification exactly, the system might return houses in Santa Ana (a city near Irvine) or 2 bedroom houses in Irvine or more expensive houses as the top matches to the query. An important aspect of top k queries is user subjectivity [20, 10]. In Example 1, let us assume that there are just 2 features: color and texture. Let Q = hQC ; Q T i be the query image where QC = 0:2; 0:4) and Q T = 0:4; 0:5) are the color and texture vectors extracted from Q (both feature spaces are 2 dimensional) Let us consider two objects in the ....

....the system will compute the new query objects and the new weights and execute the refined query. Another way to refine the query is that the user explicitly modifies the perception model i.e. she explicitly changes the weights of the features so as to better capture her perception of similarity [20, 10]. In either case, the user can continue 1 We use the terms attribute and feature interchangeably in this paper. 2 In this paper, we assume that all the feature spaces are metric and there exists an index (called the Feature index or F index) on each feature space. An F index is either single ....

[Article contains additional citation context not shown here]

A. Motro. Vague: A user interface to relational databases that permits vague queries. ACM Transactions on Office Information Systems, Vol.6, No. 3, July 1988.

....or 2 bedroom houses in Irvine or more expensive houses as the top matches to the query. This work was supported by NSF CAREER award IIS 9734300, and in part by the Army Research Laboratory under Cooperative Agreement No. DAAL01 96 2 0003. An important aspect of top queries is user subjectivity [13, 8]. In Example 1, let us assume that there are just 2 features: color and texture. Let be the query image where and are the color and texture vectors extracted from (both feature spaces are 2 dimensional) Let us consider two objects in the database: and . Let and while and . Assuming the distance ....

....the system will compute the new query objects and the new weights and execute the refined query. Another way to refine the query is that the user explicitly modifies the perception model i.e. she explicitly changes the weights of the features so as to better capture her perception of similarity [13, 8]. In either case, the user can continue refining the query over as many iterations she wants till she is satisfied with the results. Recent work shows that query refinement techniques significantly improve the quality of answers and answers improve with more iterations of feedback [23, 18, 11] ....

[Article contains additional citation context not shown here]

A. Motro. Vague: A user interface to relational databases that permits vague queries. ACM Transactions on Office Information Systems, Vol.6, No. 3, July 1988.

....or 2 bedroom houses in Irvine or more expensive houses as the top matches to the query. This work was supported by NSF CAREER award IIS 9734300, and in part by the Army Research Laboratory under Cooperative Agreement No. DAAL01 96 2 0003. An important aspect of top k queries is user subjectivity [13, 8]. In Example 1, let us assume that there are just 2 features: color and texture. Let Q = hQC ; Q T i be the query image where QC = 0:2; 0:4) and Q T = 0:4; 0:5) are the color and texture vectors extracted from Q (both feature spaces are 2 dimensional) Let us consider two objects in the ....

....the system will compute the new query objects and the new weights and execute the refined query. Another way to refine the query is that the user explicitly modifies the perception model i.e. she explicitly changes the weights of the features so as to better capture her perception of similarity [13, 8]. In either case, the user can continue refining the query over as many iterations she wants till she is satisfied with the results. Recent work shows that query refinement techniques significantly improve the quality of answers and answers improve with more iterations of feedback [23, 18, 11] ....

[Article contains additional citation context not shown here]

A. Motro. Vague: A user interface to relational databases that permits vague queries. ACM Transactions on Office Information Systems, Vol.6, No. 3, July 1988.

....model is in the ORDER BY clause. This clause indicates that we are interested in only the k answers that best match the given WHERE clause, according to the Score function. Section 4 discusses how we will evaluate top k queries for di#erent definitions of the Score function. 3 Related Work Motro [9] emphasized the need to support approximate and ranked matches in a database query language. He extended the language Quel to distinguish between exact and vague predicates. He also suggested a composite scoring function to rank each answer. Motro s work led to further development of the idea of ....

A. Motro. VAGUE: A user interface to relational databases that permits vague queries. ACM Transactions on O#ce Information Systems, 6(3):187--214, July 1988.

....in general, all require non traditional database support. They are characterized by large data sequences such as time series. Also, users of this data are typically searching for certain patterns of behavior that they approximately visualize, rather than for specific values. Previous work such as [Mo88, SW94, WZ94, ChS94], regards vague and approximate queries, as queries to which the answers are not exactly what was asked for. The query defines an exact result, in terms of specific values, which is the best we can expect. The actual results, however, are within some measurable distance, often expressed by a ....

....from some r 2 S by a transformation that is not completely feature preserving. The above definition describes an approximation notion since it abstracts away from particular values and allows us to talk about how things approximately look . It generalizes the standard notion of approximation ( [Mo88, ChS94]) in the following ways: ffl We generalize what the query denotes, from a single sequence (or a set closed under identity of values) to a set of similar sequences, which can be obtained from an exemplar through similarity preserving transformations. Thus we define approximate queries and not ....

A. Motro, VAGUE: A User Interface to Relational Databases that Permits Vague Queries, ACM Transactions on Office Information Systems, Vol. 6, No. 3, July 1988, pp. 187-214. 28

....of existing systems concerns the deterministic approach followed in query interpretation: a unique meaning is assigned to the query by the system. This limitation is addressed in the area of approximate questioning, where queries are allowed that do not specify exactly the desired result [23]. A number of research projects are active on the use of natural language for query formulation and disambiguation in restricted domains (e.g. 4, 14] Visual languages may provide new handles to tackle and overcome some of the described bottlenecks (Section 4 lists a number of significant ....

MOTRO, A. 1988. VAGUE: A User Interface to Relational Databases that permits Vague queries. ACM Trans. OIS, 6, 3, 187-214.

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Amihai Motro. Vague: a user interface to relational databases that permits vague queries. ACM Trans. Inf. Syst., 6(3):187--214, 1988.

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A. Motro. Vague: A user interface to relational databases that permits vague queries. ACM Transactions on Office Information Systems, 6(3):187--214, 1998.

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A. Motro. Vague: A user interface to relational databases that permits vague queries. ACM Transactions on Office Information Systems, 6(3):187--214, 1998.

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A. Motro. Vague: A user interface to relational databases that permits vague queries. ACM Transactions on O#ce Information Systems, 6(3):187--214, 1998.

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A. Motro. Vague: A user interface to relational databases that permits vague queries. ACM Transactions on O#ce Information Systems, 6(3):187--214, 1998.

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Amihai Motro. Vague: a user interface to relational databases that permits vague queries. ACM Trans. Inf. Syst., 6(3):187--214, 1988.

No context found.

A. Motro. VAGUE: A User Interface to Relational Databases that Permits Vague Queries. ACM Trans. on Office Information Systems, 6(3):187--214, July 1988.

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