In now classic article, James Clifford offers a novel perspective on ethnographic texts. Inspired by literary studies (e.g. Jacques Derrida's grammatology) he uses contemporary ethnographic works to question ethnography's claims of scientific objectivity and a clear distinction between allegorical and factual. If ethnography aims to keep its contemporary relevance, it should specifically focus on allegory as an intrinsic quality of ethnographic texts This kind of analysis may assume that any ethnographic text accounts for facts and events but at the same time it tackles the moral, ideological or even cosmological issues. According to Clifford, ethnography has been dominated by a "pastoral" allegorical register which allowed an ethnographer to occupy a privileged position to interpret other, non-writing cultures. Clifford notices that this register is loosing support in the modern world since the difference between illiterate and literate cultures is not relevant anymore. Ethnographic pastoral is now replaced with self-reflexive and dialogical forms of ethnographic writing, analyzed by Clifford by the example of Marjorie Shostak's book Nisa: The Life and Words of a !Kung Woman.

OceaniaVolume 79, Issue 3 p. 238-249 Hau'ofa's Hope Association for Social Anthropology in Oceania 2009 Distinguished Lecture James Clifford, James Clifford University of California Santa CruzSearch for more papers by this author James Clifford, James Clifford University of California Santa CruzSearch for more papers by this author First published: 02 January 2013 https://doi.org/10.1002/j.1834-4461.2009.tb00062.xCitations: 16AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume79, Issue3November 2009Pages 238-249 RelatedInformation

Numerous proposals for extending the relational data model to incorporate the temporal dimen- sion of data have appeared in the past several years. These proposals have differed considerably in the way that the temporal dimension has been incorporated both into the structure of the extended relations of these temporal models and mto the extended relational algebra or calculus that they define. Because of these differences, it has been difficult to compare the proposed models and to make judgments as to which of them might in some sense be equivalent or even better. In thm paper we define temporally grouped and temporally ungrouped historical data models and propose two no'uons of hzstorma 1 relational completeness, analogous to Codd's notion of relational completeness, one for each type of model. We show that the temporally ungrouped models are less expressive than the grouped models, but demonstrate a techmque for extending the ungrouped models with a grouping mechamsm to capture the additional semantic power of temporal grouping. For the ungrouped models, we define three different languages, a logic with explicit reference to time, a temporal logic, and a temporal algebra, and motwate our choice for the first of these as the basin for completeness for these models. For the grouped models, we define a many-sorted logic with variables over ordinary values, hmtorlcal values, and times. Finally, we demonstrate the equivalence of this grouped calculus and the ungrouped calculus extended with a grouping mechanism. We believe the classification of hmtorical data models into grouped and ungrouped models provides a useful framework for the comparison of models in the hterature, and furthermore, the exposition of eqmvalent languages for each type provides reasonable standards for common, and minimal, notions of historical relational completeness.

Numerous proposals for extending the relational data model to incorporate the temporal dimension of data have appeared during the past several years. These have ranged from historical data models, incorporating a valid time dimension, to rollback data models, incorporating a transaction time dimension, to bitemporal data models, incorporating both of these temporal dimensions. Many of these models have been presented in a non-traditional fashion, allowing the use of variables at the instance level. Unfortunately, the precise semantics of these database objects, e.g. tuples, with variables has not been made clear. In this paper we propose a framework for providing a formal specification of the precise semantics of this type of database, which we call a variable database. In addition, since more than one possible interpretation can be given to the specific temporal variables, such as now and ∞, which have appeared in the literature, we discuss several alternative semantics that can be given to these temporal variable databases incorporating one or more of these variables. We also present a constraint on the way such databases are allowed to evolve in time if they are to support a rollback operator.

The ambivalent legacy of anthropologists' relations with local communities presents contemporary researchers with both obstacles and opportunities. No longer justifiable by assumptions of free scientific access and interpersonal rapport, research increasingly calls for explicit contract agreements and negotiated reciprocities. The complex, unfinished colonial entanglements of anthropology and Native communities are being undone and rewoven, and even the most severe indigenous critics of anthropology recognize the potential for alliances when they are based on shared resources, repositioned indigenous and academic authorities, and relations of genuine respect. This essay probes the possibilities and limits of collaborative work, focusing on recent Native heritage exhibitions in south-central and southwestern Alaska. It also discusses the cultural politics of identity and tradition, stressing social processes of articulation, performance, and translation.

After Writing Culture: Epistemology and Praxis in Contemporary Anthropology. Allison James. Jenny Hockey. and Andrew Dawson. eds. New York: Routledge, 1997. 274 pp.

for time point variables, such as the variable t above. This approach of using time points is more declarative than ours because it hides the implementation detail of using a set of pairs of time intervals and values to implement the partial function from time points to values. It is not clear, however , whether this higher level of abstraction leaves more opportunities for optimization or whether it is more user-friendly. We believe that their approach is not well-suited to version control, since version control inherently requires to associate one time interval with a version. Another temporal OODB model is proposed by Cheng and Gadia [3]. Their temporal OODB language, called TempSQL, is based on temporal expressions, which map boolean predicates to temporal domains. For example, the temporal expression e.salary>15K]] returns the time period (lifespan) in which the salary of the employee e was greater than 15K. Such temporal expressions have also been used in the temporal query language GORDAS proposed by Elmasri and Wuu for the extended Entity-Relationship model [6]. 5 Conclusion We have presented an extension to the ODMG-93 model that incorporates a time dimension to data. In contrast to other models, time dimension in our model is an orthogonal property of data types. We have also presented an extension to OQL to accommodate time information. These language extensions are minimal, uniform, and easy to understand. As a future work, we are planning to extend our model with parameterized types for representing transaction time and bi-temporal queries, and for representing time-series data and calendars. In addition, we are planning to design an optimization framework to evaluate the TOQL language constructs efficiently using currently proposed temporal evaluation techniques. We are also planning to work on the specification of the temporal integrity constraints and their use on semantic query optimization. parameterized type Temporal h T i f Structure f Time Interval valid time, Integer version, T value g Temporal value; attribute Listh Temporal value i history; attribute Integer size; attribute Temporal value latest version; Temporal value version projection (in Short version) raises (no such version); Temporal value time projection (in Time time) raises (not deened at that time); Temporalh T i interval projection (in Time Interval i); new version (in T value, in Time event); close version (in Time event); g; The TOQL syntactic constructs are translated into the following OQL expressions: time]) struct(start: time; end: time) time 1 ; …

This document contains definitions of a wide range of concepts specific to and widely used within temporal databases. In addition to providing definitions, the document also includes separate explanations of many of the defined concepts. Two sets of criteria are included. First, all included concepts were required to satisfy four relevance criteria, and, second, the naming of the concepts was resolved using a set of evaluation criteria. The concepts are grouped into three categories: concepts of general database interest, of temporal database interest, and of specialized interest. This document is a digest of a full version of the glossary 1 . In addition to the material included here, the full version includes substantial discussions of the naming of the concepts.The consensus effort that lead to this glossary was initiated in Early 1992. Earlier status documents appeared in March 1993 and December 1992 and included terms proposed after an initial glossary appeared in SIGMOD Record in September 1992. The present glossary subsumes all the previous documents. It was most recently discussed at the "ARPA/NSF International Workshop on an Infrastructure for Temporal Databases," in Arlington, TX, June 1993, and is recommended by a significant part of the temporal database community. The glossary meets a need for creating a higher degree of consensus on the definition and naming of temporal database concepts.

Although “ now ” is expressed in SQL and CURRENT_TIMESTAMP within queries, this value cannot be stored in the database. How ever, this notion of an ever-increasing current-time value has been reflected in some temporal data models by inclusion of database-resident variables, such as “ now ” “ until-changed, ” “**,” “@,” and “-”. Time variables are very desirable, but their used also leads to a new type of database, consisting of tuples with variables, termed a variable database.

Numerous proposals for extending the relational data model to incorporate the temporaldimension of data have appeared over the past decade. It has long been known that theseproposals have adopted one of two basic approaches to the incorporation of time into theextended relational model. Recent work formally contrasted the expressive power of these twoapproaches, termed temporally ungrouped and temporally grouped, and demonstrated that thetemporally grouped models are more expressive. IN the temporally ungrouped models, thetemporal dimension is added through the addition of some number of distinguished attributes tothe schema of each relation, and each tuple is stamped with temporal values for these attributes.By contrast, in temporally grouped models the temporal dimension is added to the types of valuesthat serve as the domain of each ordinary attribute, and the application's schema is left intact.The recent appearance of TSQL2, a temporal extension to the SQL-92 standard based upon thetemporally ungrouped paradigm, means that it is likely that commercial DBMS's will be extendedto support time in this weaker way. Thus the distinction between these two approaches - and itsimpact on the day-to-day user of a DBMS - is of increasing relevance to the database practitionerand the database user community. In this paper we address this issue from the practicalperspective of such a user. Through a series of example queries and updates, we illustrate thedifferences between these two approaches and demonstrate that the temporally grouped approachmore adequately captures the semantics of historical data.

While now is expressed in SQL as CURRENT-TIMESTAMP within queries, this value cannot bestored in the database. However, this notion of an ever-increasing current-time value has beenreflected in some temporal data models by inclusion of database-resident variables, such asnow, until-changed, ∞, @ and -. Time variables are very desirable, but their usealso leads to a new type of database, consisting of tuples with variables, termed a variabledatabase.This paper proposes a framework for defining the semantics of the variable databases of temporalrelational data models. A framework is presented because several reasonable meaningsmay be given to databases that use some of the specific temporal variables that have appearedin the literature. Using the framework, the paper defines a useful semantics for such databases.Because situations occur where the existing time variables are inadequate, two new types ofmodeling entities that address these shortcomings, timestamps which we call now-relative andnow-relative indeterminate, are introduced and defined within the framework. Moreover, the paperprovides a foundation, using algebraic bind operators, for the querying of variable databasesvia existing query languages. This transition to variable databases presented here requires minimalchange to the query processor. Finally, to underline the practical feasibility of variabledatabases, we show that database variables can be precisely specified and efficiently implementedin conventional query languages, such as SQL, and in temporal query languages, suchas TSQL2.

More and more application domains, from financial market analysis to weatherprediction, from monitoring supermarket purchases to monitoring satellite images, arebecomingly increasingly data-intensive. The result is massive that are growingat a rapid rate - it has been estimated that the worldA¢Â¬Âs electronic data almostdoubles every year. With this rate of data explosion, there is a pressing need for computersto play an increasing role in analyzing these huge data repositories which areimpossible to penetrate manually. The challenge is to ferret out the regularities in thedata that will prove to be interesting to the user.A group in the Information Systems department at the NYU Business School hasbeen working in this area for a number of years. The focus of our project is now on thediscovery of patterns from time series data. In this paper we give an overview of thekinds of we are miningA¢Â¬Â? and the kinds of temporal patterns and rules whichwe are attempting to discover. In the first phase of this research, we have developed ataxonomy of patterns as a way to organize our research agenda. We wish to share thetaxonomy with the research community in the knowledge discovery in databases areasince we have found it useful in classifying the universe of regularities or patterns intodistinct types, that is, patterns which differ in terms of their structure and the amount6f search effort required to find them. Although the primary focus of our project ison time series data, and the examples we will present are chosen from this arena, thetaxonomy is general enough to apply to any type of data.

Abstract More and more application domains, from financial market analysis to weather prediction, from monitoring supermarket purchases to monitoring satellite images, are

The issue of periodicity is generally understood to be a desirable property of temporaldata that should be supported by temporal database models and their querylanguages. Nevertheless, there has so far not been any systematic examination of howto incorporate this concept into a temporal DBMS. In this paper we describe two conceptsof periodicity, which we call strong periodicity and near periodicity, and discusshow they capture formally two of the intuitive meanings of this term. We formallycompare the expressive power of these two concepts, relate them to existing temporalquery languages, and show how they can be incorporated into temporal relationaldatabase query languages, such as the proposed temporal extension to SQL, in a cleanand straightforward manner.

In this paper we address some of the concerns that have been expressed regarding the practicality of the temporally-grouped, or history-oriented, data modeling approach. Specifically, we address the concern over the lack of an algebra for this paradigm, by presenting such an algebra. In addition, we examine the semantics of two notions, coalescing and restructuring, from the perspective of a comparison between the temporally grouped and temporally ungrouped paradigms.

In the first part of the panel, several well-known researchers from the temporal database field discussed ongoing efforts to create an infrastructure to support temporal data management. What infrastructure now exists, or should exist soon?