With the growing focus on business analytics and data-driven decision-making, there is a greater need for humans to interact effectively with data. We propose that presenting data to human users in terms of instances and attributes provides a more flexible and usable structure for querying, exploring, and analysing data. Compared to a traditional representation, an instance-based representation does not impose any predefined classification schema over the data when it is presented to users. This paper examines the potential utility of instance-based data through two laboratory experiments – the first focusing on exploration of data for pattern discovery (open-ended tasks) and the second on retrieval of information (closed-ended tasks). In both cases, participants were able to achieve better results in tasks using instance-based data than using class-based representations. Given the growing need for self-service analytics, as well as using information for purposes not anticipated when it was collected, we show that instance-based representations can be an effective way to satisfy the emerging needs of information users.

While many conceptual modelling grammars have been developed since the 1970s, they share the general assumption of representation by abstraction; that is, representing generalised knowledge about the similarities among phenomena in a domain (classes) rather than about domain objects (instances). This assumption largely ignores the fundamental role that instances play in the constitution of reality and in human psychology. In this paper, we argue there is a need for a grammar that explicitly recognises the primary role of instances. We examine the limitations of traditional class-based approaches to conceptual modelling, especially for modern information environments. We then explore theoretical and practical motivations for instance-based modelling, and show how such an approach can address the limitations of traditional modelling approaches. We conclude by calling for the engineering of instance-based grammars as an important direction for conceptual modelling research to address the limitations of traditional approaches, and articulate five challenges to overcome in such efforts.

The emergence of crowdsourcing as an important mode of information production has attracted increasing research attention. In this article, the authors review crowdsourcing research in the data management field. Most research in this domain can be termed tasked-based, focusing on micro-tasks that exploit scale and redundancy in crowds. The authors' review points to another important type of crowdsourcing – which they term observational – that can expand the scope of extant crowdsourcing data management research. Observational crowdsourcing consists of projects that harness human sensory ability to support long-term data acquisition. The authors consider the challenges in this domain, review approaches to data management for crowdsourcing, and suggest directions for future research that bridges the gaps between the two research streams.

Data exchange is the process of generating an instance of a target schema from an instance of a source schema such that source data is reflected in the target. The prevailing approach for data exchange is based on schema mappings, which are high level expressions that describe relationships between database schemas [1]. However, schema-mapping based data exchange techniques suffer from two problems: (1) entity fragmentation, in which information about a single entity is spread across several tuples in the target schema, and (2) ambiguity in generalization, in which incorrect mappings result from using different methods to represent entity type generalization in source and target schemas. In this paper, we propose the Scalable Entity Preserving Data Exchange (SEDEX) method, which combines schema-level and datalevel information to address these problems. We also provide extensive evaluation to demonstrate the benefits and scalability of the approach.

Data exchange is the process of generating an instance of a target schema from an instance of a source schema such that source data is reflected in the target. Generally, data exchnge is performed using schema mapping, representing high level relations between source and target schemas. In this paper, we argue that data exchange solely based on schema level information limits the ability to express semantics in data exchange. We show such schema level mappings not only may result in entity fragmentation, they are unable to resolve some ambiguous data exchange scenarios. To address this problem, we propose Scalable Entity Preserving Data Exchange (SEDEX), a hybrid method based on data and schema mapping that employs similarities between relation trees of source and target relations to find the best relations that can host source instances. Our experiments show SEDEX outperforms other methods in terms of quality and scalability of data exchange.

The UML is an industry standard for object-oriented software engineering. However, there is little empirical evidence on how UML is used. This article reports results of a survey of UML practitioners. We found differences in several dimensions of UML diagram usage on software development projects including; frequency, the purposes for which they were used, and the roles of clients/users in their creation and approval. System developers are often ignoring the “use case-driven” prescription that permeates much of the UML literature, making limited or no use of either use case diagrams or textual use case descriptions. Implications and areas requiring further investigation are discussed.

A use case is a description of a sequence of actions constituting a complete task or transaction in an application. Use cases were first proposed by Jacobson (1987) and have since been incorporated as one of the key modeling constructs in UML (Booch, Jacobson, & Rumbaugh, 1999) and the Unified Software Development Process (Jacobson, Booch, & Rumbaugh, 1999). This paper traces the development of use cases, and identifies a number of problems with both their application and theoretical underpinnings. From an application perspective, the use case concept is marked by a high degree of variety in the level of abstraction versus implementation detail advocated by various authors. In addition, use cases are promoted as a primary mechanism for identifying objects in an application, even though they focus on processes rather than objects. Moreover, there is an apparent inconsistency between the so-called naturalness of object models and the commonly held view that use cases should be the primary means of communicating and verifying requirements with users. From a theoretical standpoint, the introduction of implementation issues in use cases can be seen as prematurely anchoring the analysis to particular implementation decisions. In addition, the fragmentation of objects across use cases creates conceptual difficulties in developing a comprehensive class model from a set of use cases. Moreover, the role of categorization in human thinking suggests that class models may serve directly as a good mechanism for communicating and verifying application requirements with users. We conclude by outlining a framework for further empirical research to resolve issues raised in our analysis.

Database design commonly assumes, explicitly or implicitly, that instances must belong to classes. This can be termed the assumption of inherent classification . We argue that the extent and complexity of problems in schema integration, schema evolution, and interoperability are, to a large degree, consequences of inherent classification. Furthermore, we make the case that the assumption of inherent classification violates philosophical and cognitive guidelines on classification and is, therefore, inappropriate in view of the role of data modeling in representing knowledge about application domains. As an alternative, we propose a layered approach to modeling in which information about instances is separated from any particular classification. Two data modeling layers are proposed: (1) an instance model consisting of an instance base (i.e., information about instances and properties) and operations to populate, use, and maintain it; and (2) a class model consisting of a class base (i.e., information about classes defined in terms of properties) and operations to populate, use, and maintain it. The two-layered model provides class independence . This is analogous to the arguments of data independence offered by the relational model in comparison to hierarchical and network models. We show that a two-layered approach yields several advantages. In particular, schema integration is shown to be partially an artifact of inherent classification that can be greatly simplified in designing a database based on a layered model; schema evolution is supported without the complexity of operations currently required by class-based models; and the difficulties associated with interoperability among heterogeneous databases are reduced because there is no need to agree on the semantics of classes among independent databases. We conclude by considering the adequacy of a two-layered approach, outlining possible implementation strategies, and drawing attention to some practical considerations.