This workshop paper focuses on exposing faculty to how the medium of board games provides an exceptional space for faculty development for our teaching and student learning. The “Let's Play” intervention [1] takes the form of a workshop where participants have opportunities to experience role reversal through being a learner again. Participants become active learners by playing board games that help them remember the experience of being a learner again. By choosing different types and styles of games, we can provide a space for the participants to discuss broader teaching practices, such as the importance of technical vocabulary, scaffolding ideas as we teach them, and the benefits of student-centered learning approaches. Another critical aspect of this intervention is that we hope to use role reversal to remind teachers how hard it is to learn in the hope that teachers will have more empathy for their learners. In this paper, we describe our workshop structure and pertaining literature and ideas on why board games are part of this medium. NOTE that many past participants are looking for how to use games in the classroom. This workshop does not address that aspect of the medium even though we have experience in this space [2].

Capstone courses are an integral part of undergraduate and postgraduate degrees in the computing fields. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self reflection as they transition into the real world. Prior research on capstone courses has primarily focused on the experiences of the students. The perspectives of instructors who teach these capstone courses has not been explored much. However, an instructor's motivation and expectancy can have a significant effect on a capstone course quality. In this working group, we plan to use a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation will be examined through a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across multiple continents. This global perspective will be used to develop a guiding framework on the different pedagogical approaches that can be used to enhance engagement and motivation for both staff and students in computing courses.

Cloud-based resources for students to create and query databases can be leveraged in a traditional database curriculum as well as in updated curriculum to support data science. This paper describes processes to apply for a Google Cloud Platform grant for educators, to establish student accounts, build a SQL database in the cloud and connect to it, and also use SQL to query a big data platform for millions of records in open source data sets. These freely available, industrial strength resources can be used for teaching SQL and database creation, as well as for introducing more advanced topics such as physical design, performance comparison, and data warehouse creation and population. Associated learning objectives, assignments, and assessment of student learning and perspectives are presented.

Over the past two decades, data science or data analytics degree programs have begun to emerge, reflecting the world's demand for data specialists to make sense of the vast amounts of collected data in the sciences, engineering, business, and other domains. As degree creation has occurred mainly due to demand, ACM and other professional bodies have recently stepped in to provide curricular guidance. However, no \em shared global framework for data science as an academic discipline exists, making growth unfocused and driven by employer demands. More recently, the growth of artificial intelligence has also impacted data science programs. This working group builds on prior efforts and participant experiences to develop a global taxonomy of approaches to data science education and expectations for graduates of data science programs to \em think like data scientists.

Widespread interest in developing and enhancing undergraduate data science education is evidenced by the interim report Envisioning the Data Science Discipline: The Undergraduate Perspective recently released by the National Academies of Science/Engineering/Medicine. The report identifies data modeling as one of the key concepts for developing and applying data acumen (making good decisions and judgements with data). We define data modeling as a process for documenting how data is connected, processed, and stored; a particular data model may address one or more of these aspects. This BoF session is proposed to focus specifically on data modeling skills for data science study. Traditional data models taught in computing curriculum include Entity-Relationship, UML, and relational models; these topics can be covered at various points in the curriculum, but are typically included in database courses. The proliferation of advanced data models and systems (for example, but not limited to, document stores, graph databases, column stores, key-value stores, and relational + map reduce) provides an opportunity for developing/enhancing database curriculum at the undergraduate level to support programs in data science. A goal of this BoF is to identify faculty who wish to develop and share best practices for teaching data modeling for data science, including course learning objectives and outcomes, techniques, and materials.

The "Girls on the Go: The Mobile Computing College Experience" is a residential summer camp whose aim is to encourage female high school students to attend college, to consider computer science as viable major, and to pursue computing related degrees. The camp content is designed primarily around a user-centered design process, with the students gaining experience in the development of a zoo-themed mobile app. In addition to technical experience, students participate in sessions that educate them on campus life, the admissions process, and more importantly, what a computing-related career may hold for them. The camp was offered in two consecutive summers, with approximately 20 participants in each iteration. In this paper, we compare differences between the two iterations with the intent of identifying both lessons learned and avenues for improving the experience. We investigate whether the camp had an effect on attitudes towards computer science as a major, and on the careers available to computer science graduates. To this end, our analysis revealed a statistically significant effect upon raising awareness of the role of computer scientists and the relevancy of computing for solving real world problems.

In this paper, we describe our experience running "Girls on the Go: The Mobile Computing College Experience." We decided to do a residential summer camp for HS-age girls to achieve two goals: to encourage our campers to attend college and to interest them in computer science as a possible career option. We centered the camp around the design of a zoo-themed research tool where campers designed a mobile application to be used by researchers to document and study animal behavior. Post-camp surveys gave statistically significant results indicating that the camp increased the girls' confidence in performing computer science and understanding what computer scientists did.

The ACM 14th International Workshop on Data Warehousing and OLAP (DOLAP 2011), held in Glasgow, Scotland, UK on October 28, 2011, in conjunction with the ACM 20th International Conference on Information and Knowledge Management (CIKM 2011), presents research on data warehousing and On-Line Analytical Processing (OLAP). The DOLAP 2011 program has three interesting sessions on data warehouse modeling and maintenance, ETL and performance, and OLAP visualization and extensions, and a panel discussing analytics in data warehouses.

In this paper, we provide an overview of state-of-the-art research issues and achievements in the field of analytics over big data, and we extend the discussion to analytics over big multidimensional data as well, by highlighting open problems and actual research trends. Our analytical contribution is finally completed by several novel research directions arising in this field, which plays a leading role in next-generation Data Warehousing and OLAP research.

The Property Map is a specialized multidimensional indexing technique that precomputes attribute expressions for selected data items and stores the results as bit strings. Property Maps effectively index multi-attribute queries and high cardinality attributes. Designing properties for a Property Map, however, is not a trivial task. The solution space has exponential complexity; thus we develop heuristic algorithms to reduce the number of solutions to consider while still producing a Property Map index with good performance. In this paper, the algorithms comprising the design framework for Property Maps are discussed and illustrated.

A data warehouse is a collection of data from different sources that supports analytical querying. A Bitmap Index (BI) allows fast access to individual attribute values that are needed to answer a query by representing the values of an attribute for all tuples separately, as bit strings. A Property Map (PMap) is a multidimensional indexing technique that pre-computes attribute expressions, called properties, for each tuple and stores the results as bit strings [DD97, LD02]. This paper compares the performance of the PMap and the Range-Encoded Bit-Sliced Index (REBSI) [CI98] using cost models to simulate their storage and query processing costs for different kinds of queries over a benchmark schema. We identify parameters that affect performance of these indexes and determine situations in which either technique gives significant improvement over the other. We also explore ways to improve PMap design to enhance performance.

No abstract available.

Article Free Access Share on Partitioned auxiliary views for self-maintainable data warehouse Authors: Mala Rajamani Database Systems Laboratory, ECECS Department, University of Cincinnati, Cincinnati, OH Database Systems Laboratory, ECECS Department, University of Cincinnati, Cincinnati, OHView Profile , Karen C. Davis Database Systems Laboratory, ECECS Department, University of Cincinnati, Cincinnati, OH Database Systems Laboratory, ECECS Department, University of Cincinnati, Cincinnati, OHView Profile Authors Info & Claims DOLAP '98: Proceedings of the 1st ACM international workshop on Data warehousing and OLAPNovember 1998 Pages 66–71https://doi.org/10.1145/294260.294274Published:01 November 1998Publication History 0citation364DownloadsMetricsTotal Citations0Total Downloads364Last 12 Months6Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Electronic Design Automation (EDA) tools, such as layout generators and simulators, have generally focused on algorithms and techniques for hardware design. Data management aspects have not been emphasized, but the volume of data, heterogeneity of data formats, and the evolution/proliferation of tools have made data modeling and data interchange increasingly important research issues. The data sharing problem stems from the fact that related EDA tools are often used in various sequences to manipulate and annotate a single design. In a typical design environment, tools use file-based data storage, with limited data modeling capabilities, and primitive or non-existent query facilities. In order to support current tools, we wish to preserve the semantics of existing hardware description languages in rigorous data models; we propose to capture each existing language in a semantic space model. We view data interchange as a query against one semantic space that produces objects, i.e., query answers, in another semantic space. We define an integrating meta-model, the meta-space, and also define general query operators for transforming objects between semantic spaces. These query operators define both the intension and extension of a query result; the transformed data is described in the type system of the meta-space, thus providing explicit semantics for the shared data. Our modeling approach supports advanced and evolving applications, such as hardware/software codesign, through the ability to retrieve data resident in individual semantic spaces, as well as to share data in semantic spaces from different EDA sources.

The philosophy of explicitly representing simple range classes for properties and explicitly specifying range and/or cardinality restrictions on inherited properties is adopted so that the membership of an object in classes is characterized by the object's property values. The motivation for this approach is twofold: first, it is in keeping with the database philosophy of emphasizing the representation of an application (rather than some intrinsic meaning); and second, it provides a framework for algorithmically verifying the structural aspects of the schema. An automatic classifier for the structural validation of object-oriented schemas based on sound and complete rules of inference is presented. If the classifier discovers structural relationships and/or inconsistencies, the designer can refine the schema to reflect the semantics of the application. Thus the classifier serves as an interactive design tool that can be used at any stage during the design process.>