For the past two years, we have implemented a new professional development (PD) course for high school Career and Technical Education (CTE) teachers.The course meets for an entire academic year and is divided into two phases."Phase 1" (Aug-Dec) focuses on CS content knowledge by preparing participants to take the Praxis CS 5652 test, a pathway in many states for K-12 teachers to obtain an add-on CS endorsement for their teaching licenses."Phase 2" (Jan-May) focuses on pedagogical content knowledge as participants complete a self-directed project related to their individual CS teaching goals.We strive to deliver personalized PD that is effective in training high school teachers from a variety of education and CS skills backgrounds to teach advanced CS courses. This poster considers the following research question:To what extent do teachers who participate in our PD develop (a) self-efficacy as a well-prepared teacher of advanced CS content and (b) identity as a CS teacher?We know that CS teacher self-efficacy, specifically teacher perceptions of preparation and readiness, is required for teachers to effectively implement a CS curriculum [3].Furthermore, CS teacher professional identity provides a foundation for sustained motivation, job satisfaction, and commitment [1].We are particularly interested in how CTE teachers from a variety of backgrounds and skill levels perceive themselves as CS teachers, and what impact our PD has on the development of their identity as CS teachers.We are conducting a mixed-methods study that includes surveying and interviewing participants before, during, and after the PD course.All responses are linked so that we can measure changes in self-efficacy and identity.We administered the Computer Science Teacher Identity Survey [2] at three points.Before the course, participants complete an intake interview to capture prior knowledge and learning goals.In the middle of the course, we interview participants about their experience preparing for and taking the Praxis test.After the course, participants complete an exit interview about

Numerous studies have shown that active learning and student-centered pedagogies lead to better student outcomes, such as higher grades, enhanced self-efficacy, and an increased sense of belonging. These outcomes are closely linked to higher levels of student engagement. To better understand the relationship between student engagement and pedagogical approach, our research documents what actually happens in CS1 classes that implement active learning. This paper presents a case study of two instructors, at different undergraduate institutions, teaching with Process Oriented Guided Inquiry Learning (POGIL) and Interactive Lectures. Using structured classroom observations and student surveys, we measure the engagement of the same students in different class periods taught by the same instructor. Our study investigates the differences and similarities in self-reported and observed student behaviors, as well as observed instructor behaviors. We examine how instructor behavior impacts student behavior. The results show significant differences in observed instructor and student behaviors based on the pedagogical approach. In class periods where instructors spoke more, students were more inclined to watch or listen rather than actively work or discuss, coupled with higher levels of student distraction. Our results provide insight into how specific teaching practices can lead to more engaging classrooms and better student outcomes.

We are collaborating with a nonprofit organization to train high school Career Technical Education (CTE) teachers to teach computer science courses. In the United States, many CTE teachers are not automatically licensed to teach CS. The traditional pathway to licensure requires 18 credits of CS at a university, which is impractical for teachers while working full-time. Another pathway to licensure is taking the Praxis CS test, which in many states gives teachers an add-on endorsement in computer science. One of the biggest challenges for our teachers is learning how to program with the pseudocode used on the test. We have created an online development environment, named Praxly, that visualizes and runs the pseudocode, providing a space in which teachers can practice. Praxly supports both a text-based editor and a block-based editor, and the editors are synchronized with each other. This poster highlights Praxly's features and implementation details, outlines early research results, and discusses next steps for the project.

From 2017-2022, our research project supported faculty at higher-ed institutions in the United States to adopt POGIL in CS1 courses. The faculty participated in summer workshops and mentoring groups during the academic year. At the end of each term, the faculty submitted a summary of their students' grades to the research team. This paper presents a Bayesian analysis of the student grades using a hierarchical ordinal logistic regression model. The data included the number of A, B, C, D, F, and W grades, disaggregated by gender and race, for all students enrolled in the course. In addition to each POGIL term, faculty submitted grades for one or two previous terms when they taught the same course without POGIL. Most faculty observed an improvement in student pass rates in the second and third term after they began teaching with POGIL. We present detailed visualizations of grade distributions from 25 faculty, along with the results of the statistical analysis. Our model suggests that CS1 faculty adopting POGIL can expect to see a modest increase of A grades and a modest decrease of DFW grades. However, the grades of Black, Hispanic, and Indigenous students decreased slightly, especially in the first term faculty taught with POGIL. The results of this study demonstrate the importance of gender and racial analysis in evaluating pedagogical approaches.

The process of sharing content among instructors at different institutions is not straightforward. In most contexts, "shared" material is unidirectional: a more experienced instructor shares their materials with a more novice instructor; a book publisher provides resources to instructors who have adopted their textbook. In a fully-realized sharing ecosystem, this flow is bi-directional. Materials can be shared, modified, corrected or edited, and then the changes are committed back to the repository for use by everyone who has adopted the material. There are many issues that must be addressed regarding the sharing process, related to both the actual content, as well as the context in which the sharing occurs. In fact, there is a wide opinion on what exactly "sharing" means. As such, we endeavoured to identify sharing opportunities and address issues that sharing course content raises. The primary goal of this initiative was to inform the development of ways in which shared courses and content can be made available online to share across higher education institutions in Virginia, as a potential model to be expanded to other localities and disciplines.

Employers regularly list teamwork as one of the most desirable skills they are seeking in college graduates. This paper describes a study about the effect of teamwork on student learning and classroom culture in a CS1 college class. In Fall 2021, an experienced college professor taught with POGIL for the first time in three sections of CS1. In two sections, students completed the POGIL activities in teams. In the third section, students completed the same POGIL activities individually. This study was then repeated in Spring 2022. In the fall semester, we observed a noticeable effect on classroom culture but no differences in student learning. In the spring semester, students in the sections with POGIL teams performed better on quizzes and programming assignments than those in the section where students worked individually. We discuss the implications of these results on collaborative learning in CS courses and the importance of student teams for faculty adopting POGIL.

Process Oriented Guided Inquiry Learning (POGIL) is becoming increasingly popular in computer science as an evidence-based teaching strategy. From 2017--2022, the IntroCS-POGIL project supported faculty at higher-ed institutions in the United States to adopt POGIL in their introductory CS courses. The faculty participated in summer workshops and mentoring groups during the academic year. At the end of each semester, the faculty submitted a summary of their students' grades to the research team. This poster presents a Bayesian analysis of the student grades using a hierarchical ordinal logistic regression model. The data included the number of A, B, C, D, F, and W grades, disaggregated by gender and ethnicity, for all students enrolled in the course. In addition to each POGIL semester, faculty submitted grades for one or two previous semesters when they taught the same course without POGIL. The poster will summarize the regression model and present a detailed visualization of 12 faculty that highlight positive and negative results.

To encourage and support faculty to adopt active learning pedagogies, we need to understand: (1) what faculty perceive as the challenges and benefits of adoption; and (2) how professional development and support affect faculty adoption. This paper describes a project to provide professional development and support for faculty planning to adopt POGIL in their introductory CS classes. Process Oriented Guided Inquiry Learning (POGIL) is an evidence-based, student-centered pedagogy that focuses on developing both content knowledge and process skills. Using workshop evaluations, interviews, and an end-of-term survey, we found that participating faculty appreciated the additional training, learning materials, and mentoring. They reported that they implemented most key elements of POGIL and that POGIL enhanced student engagement and learning. Key challenges include the time required to complete in-class activities, cover course content, and prepare for activities. These results should help to improve professional development for faculty who want to adopt evidence-based strategies to improve student outcomes.

For the past ten years, computer science instructors have adopted Process Oriented Guided Inquiry Learning (POGIL). Other STEM disciplines have shown conclusively that POGIL impacts student learning and knowledge retention. However, most research about POGIL in computer science has focused on perceptions and experiences, not learning outcomes. In this study, we examined the influence of POGIL on student learning in CS1. We collected data from all sections of CS1 at the same institution. Four of the faculty implemented POGIL, and three taught with other active methods. The learning data included pre and post assessments, midterm and final exams, and a retention test at the beginning of the next course. Students also completed three surveys about their prior programming experience, sense of belonging, and perceptions of teamwork. We used multiple regression to analyze the relationship between the survey data and learning outcomes. Our results show that students in the POGIL sections outperformed students in the other sections. POGIL students scored higher on the post-test, and a higher proportion of them met the grade requirement to progress to the next course. After the five-week winter break, POGIL students had higher and more consistent scores on the retention test. These results provide evidence that POGIL can be very effective as an instructional technique in computer science.

The expense of textbooks is a common source of frustration among students. Furthermore, the lack of affordable textbooks can inadvertently limit who studies computer science. One way educators and faculty can mitigate these issues is through adopting (and writing) free online textbooks. This panel discusses the benefits, challenges, and development practices of free online textbooks. The panelists, who have authored three widely used textbooks, characterize the role of free textbooks in CS education, describe their experiences writing a free textbook, and offer advice to faculty interested in incorporating them into their courses.

Prior research has shown that a sense of belonging is very important for students to continue in their field of study. This is particularly true for minoritized students in introductory CS courses. The methods used so far to study sense of belonging in CS have been qualitative or have used a small number of survey items. In this paper, we validate a large 30-item survey originally developed for Mathematics. We collected data from 21 institutions, with 30 faculty and 1165 survey responses. We present a factor analysis for the survey to highlight how it measures factors like 'Membership', 'Acceptance', 'Affect', 'Trust' and 'Desire to Fade'. We measured the students' sense of belonging using the survey and found that it correlates with their learning and their interest to pursue more CS courses. These correlations were nuanced based on students' gender, race, and the type of learning environment. We found that for minoritized students in particular, the interest to pursue CS courses was more correlated with their sense of belonging. A type of learning environment we examined was Process Oriented Guided Inquiry Learning (POGIL). We studied the differences in sense of belonging between POGIL and non-POGIL classrooms. Although we did not find a significant difference in overall sense of belonging at the factor level, we did find significant differences when we examined at the item level. These results shed light about nuances in students' sense of belonging in CS POGIL classrooms when compared to other approaches.

Collaborative learning actively engages students in the learning process while developing professional skills that employers seek. Approaches such as Pair Programming and Peer Instruction have been shown to improve student outcomes in computer science. Process Oriented Guided Inquiry Learning (POGIL) is another col- laborative learning approach that aims to develop students' mastery of discipline-specific concepts as well as 'process skills' like com- munication, teamwork, and critical thinking. This paper examines students' perceptions of their content learning and process skills in courses that recently switched to POGIL. We explore the re- lationship between those perceptions, self-efficacy, and learning performance. Our results show that students have positive views about their teamwork, developing skills to work with others, and thinking through a problem. However, self-efficacy is the only sig- nificant predictor of their learning. We discuss the implications of these results for using POGIL and other collaborative learning in CS courses, and identify directions for future work.

Process-Oriented Guided Inquiry Learning (POGIL) is a research-based instructional strategy with a proven history across STEM disciplines. In a POGIL classroom, teams of students work on activities that are specifically designed to guide them to construct their own understanding of key concepts. At the same time, students develop process skills such as communication, teamwork, problem solving, and critical thinking. POGIL incorporates practices shown to be particularly helpful for students from underrepresented populations. In a POGIL classroom, teachers are facilitators, not lecturers. Multiple studies have shown that students in POGIL classes do better on common exams and in subsequent courses. For more information, see http://cspogil.org and http://pogil.org. This BOF will (1) enable people unfamiliar with POGIL to ask questions and learn more, and (2) bring together experienced POGIL practitioners to share experiences, concerns, ideas, and insights. As in a POGIL classroom, we will discuss topics in small teams and report out to the larger group.

This workshop introduces Process Oriented Guided Inquiry Learning (POGIL) to anyone who teaches CS or related subjects. In a POGIL classroom, teams of 3-4 learners work on activities with a particular structure based on learning cycles. Through scripted inquiry and investigation, learners discover concepts and construct their own knowledge. Using assigned team roles and other scaffolding, learners develop process skills and individual responsibility. The teacher is not a lecturer, but an active facilitator who helps all students to be engaged and achieve the learning objectives.POGIL is an evidence-based approach that has been shown to improve student performance significantly. Workshop participants will work through POGIL activities as students and complete meta-activities designed to introduce core POGIL concepts, practices, and benefits. We will share POGIL materials for a variety of CS courses. For more information, see http://IntroCSpogil.org and http://pogil.org, where you will find activities for CS1, CS2, and other courses.

Process skills (also known as professional skills, lifelong learning skills, workplace skills, transferable skills, or soft skills) are an important aspect of computer science education. Learning objectives for computer science courses often include the development of process skills like critical thinking, problem solving, and teamwork, but many instructors struggle with how to encourage and measure the development of these skills. This special session will introduce detailed rubrics for assessing process skills in a manner that will model POGIL (Process Oriented Guided Inquiry Learning) facilitation techniques. Presenters will demonstrate how to use one set of these rubrics in an active learning classroom (i.e., POGIL) to provide students with feedback. The other set of rubrics assess process skills on student work and can be used with any classroom. Attendees will leave the session with a better understanding of key process skills in computer science, how to assess student interactions and student work for these skills, and how a POGIL classroom facilitates the development of critical thinking, information processing, and teamwork. This special session will appeal to any K-12 teacher or college instructor interested in developing students' process skills in a computer science class.

This workshop introduces Process-Oriented Guided Inquiry Learning (POGIL) to anyone who teaches CS or related subjects. In a POGIL classroom, teams of 3-4 learners work on activities with a particular structure based on learning cycles. Through scripted inquiry and investigation, learners discover concepts and construct their own knowledge. Using assigned team roles and other scaffolding, learners develop process skills and individual responsibility. The teacher is not a lecturer, but an active facilitator who helps all students to be engaged and achieve the learning objectives. POGIL is an evidence-based approach that has been shown to improve student performance significantly. Workshop participants will work through POGIL activities as students and complete meta-activities designed to introduce core POGIL concepts, practices, and benefits. We will share POGIL materials for a variety of CS courses. For more information, see pogil.org and cspogil.org, where you will find activities for CS1, CS2, and other courses. Laptops optional.

Introductory computer science courses face multiple challenges, including a broad range of content, diverse teaching methods, and the need to help students develop skills such as problem solving, critical thinking, and teamwork. One evidence-based approach to address these challenges is Process Oriented Guided Inquiry Learning (POGIL), in which student teams work on classroom activities specifically designed to help them construct understanding and develop key skills. While POGIL has been widely used and studied in Chemistry and other STEM fields, much less is known about how it is used in Computer Science. In this study, we examined how faculty adopt POGIL for the first time in introductory CS classrooms. Using qualitative in-depth interviews, we investigated why faculty chose to adopt POGIL and their concerns about using it. Our results suggest that faculty motivations to use POGIL centered around improving student outcomes, including their learning and engagement. However, faculty also had concerns about using POGIL, which ranged from how POGIL impacts the curriculum to logistical and institutional barriers. We discuss the implications of these findings with respect to faculty development and active learning pedagogies.

his workshop introduces Process-Oriented Guided Inquiry Learning (POGIL) to anyone who teaches CS or related subjects. In a POGIL classroom, teams of 3-4 learners work on activities with a particular structure based on learning cycles. Through scripted inquiry and investigation, learners discover concepts and construct their own knowledge. Using assigned team roles and other scaffolding, learners develop process skills and individual responsibility. The teacher is not a lecturer, but an active facilitator who helps all students to be engaged and achieve the learning objectives. POGIL is an evidence-based approach, and has been shown to significantly improve student performance. Workshop participants will work through POGIL activities as students, and work through POGIL meta-activities that are designed to help teachers learn core POGIL concepts, practices, and benefits. We will share POGIL materials for a variety of CS courses and concepts. For more information (including activities for CS1, CS2, and other courses), see http://IntroCSpogil.org and http://pogil.org. Laptops optional.

POGIL (Process Oriented Guided Inquiry Learning) is a form of active learning where students work collaboratively through activities designed to guide them to discover concepts on their own. POGIL is based on the principle that students will learn and retain more when they construct their own understanding of the computer science (CS) content. Working in groups of three to four, students develop process skills, such as problem solving, communication, and teamwork. This special session will first provide SIGCSE attendees the opportunity to experience a CS POGIL activity for themselves. We will then discuss different ways that instructors might adopt POGIL for use in their classroom, and how the adoption of POGIL by multiple instructors in a department can change the culture of the department.

Before Fall 2013, our CS majors were required to take the same 4-credit introductory programming course as part of a two-semester CS1 designed to be welcoming to novices. As CS in K-12 has expanded, the diversity of incoming students' programming backgrounds has increased, raising concerns that the climate was becoming increasingly intimidating for novices. The literature suggests these effects could disproportionately impact retention of female students and other underrepresented minorities, undermining other efforts to broaden participation in computing. To address these concerns, we split the first course based on students' prior programming experience. Using statistical techniques, we analyzed the intermediate quantitative impact of this and other curricular changes. Our results suggest that adding the alternative CS1 course had little effect on retention, but the overall structure has been successful in achieving comparable CS2 outcomes regardless of prior experience.

This workshop introduces Process-Oriented Guided Inquiry Learning (POGIL) to anyone who teaches CS or related subjects. In a POGIL classroom, teams of 3-5 learners work on activities with a particular structure based on learning cycles. Through scripted inquiry and investigation, learners discover concepts and construct their own knowledge. Using assigned team roles and other scaffolding, learners develop process skills and individual responsibility. The teacher is not a lecturer, but an active facilitator who helps all students to be engaged and achieve the learning objectives. POGIL is an evidence-based approach, and has been shown to significantly improve student performance. Workshop participants will work through POGIL activities as students, and work through POGIL meta-activities that are designed to help teachers learn core POGIL concepts, practices, and benefits. We will share POGIL materials for a variety of CS courses and concepts. For more information, see http://cspogil.org and http://pogil.org, including activities for CS1, CS2, and other courses. Laptops optional.

With the recent launch of AP CS Principles in 2016-17, many efforts are currently underway to share curriculum resources and prepare new teachers. The community has primarily focused on high school implementations, which have different situational factors than university courses (e.g., amount of class time). In this poster, we present the design of a survey course that aligns with CS Principles and also continues the long tradition of breadth-first introductions to computer science at the college level. We describe the instructional strategies, assessments, and curriculum details, providing a model for how to modify existing CS0 courses. We also outline twelve lab activities that support the computational thinking practices and learning objectives of the AP curriculum framework. All instructional materials including activities, labs, performance tasks, and rubrics are freely available on the course website: https://w3.cs.jmu.edu/cs101. Quizzes, solutions, and other materials are also available to instructors upon request. The course has run successfully for the past four years at two universities and three high schools via dual enrollment. Initial results suggest that the curriculum has a positive impact on student confidence levels and attitudes toward computer science.

This paper presents an analysis of CS faculty perceptions of the benefits of POGIL, the obstacles to POGIL adoption, and opportunities for professional development. Participants strongly agreed that with POGIL, students are more engaged and active, develop communication and teamwork skills, and have better learning outcomes. The largest perceived obstacle was lack of preparation time; other obstacles included availability of relevant POGIL activities and pressure to cover more content. Participants expressed a desire for further training and mentoring beyond workshops. Our data analysis also considers bivariate associations and interactions. The results should help to improve professional development for CS faculty adopting evidence-based strategies, and thereby help more CS students to be successful.

This workshop is for anyone who teaches CS, and introduces process-oriented guided inquiry learning (POGIL) in computer science. In a POGIL classroom, teams of 3-5 learners work on activities with active facilitation by the teacher. Through scripted inquiry and investigation, learners discover concepts and construct their own knowledge. Using assigned team roles and other scaffolding, learners develop process skills and individual responsibility. In a POGIL classroom, the teacher is not a lecturer, but a facilitator who helps to ensure that all students are engaged and able to achieve the learning objectives. POGIL activities have a particular structure based on learning cycles. POGIL is a research-based instructional strategy, and studies show that it can significantly improve student performance. Workshop participants will work through POGIL activities as students. Participants will also work through meta-activities that use POGIL and are designed to help teachers learn core POGIL concepts, practices, and benefits. POGIL materials for a variety of CS concepts will be shared. More information is available at http://pogil.org and http://cspogil.org, including sample activities for CS1, CS2, and other courses. Laptops optional.

Process-Oriented Guided Inquiry Learning (POGIL) is a research-based instructional strategy with a proven history across STEM disciplines. In a POGIL classroom, teams of students work on activities that are specifically designed to guide them to construct their own understanding of key concepts. At the same time, students develop process skills such as communication, teamwork, problem solving, and critical thinking. POGIL incorporates practices shown to be particularly helpful for students from underrepresented populations. In a POGIL classroom, teachers are facilitators, not lecturers. Multiple studies have shown that students in POGIL classes do better on common exams and in subsequent courses. For more information, see http://cspogil.org and http://pogil.org. This BOF will (1) enable people unfamiliar with POGIL to ask questions and learn more, and (2) bring together experienced POGIL practitioners to share experiences, concerns, ideas, and insights. As in a POGIL classroom, we will discuss topics in small teams and report out to the larger group.

Process-Oriented Guided Inquiry Learning (POGIL) is a research-based instructional strategy with a proven history across STEM disciplines. In a POGIL classroom, teams of students work on activities that are specifically designed to guide them to construct their own understanding of key concepts. At the same time, students develop key process skills such as communication, teamwork, problem solving, and critical thinking. For example, an activity might start with a simple game, and questions would guide teams to identify, analyze, and compare algorithms to play the game, leading them to develop techniques and identify issues and tradeoffs. POGIL incorporates practices shown to be particularly helpful for students from underrepresented populations. In a POGIL classroom, teachers are facilitators, not lecturers. Multiple studies have shown the students in POGIL classes do better on common exams and in subsequent courses.

Relational algebra provides a theoretical foundation for how modern database management systems optimize and execute queries. Its main concepts are based on set theory and first order logic, which can be challenging for students to learn due to their abstract nature. This paper presents Bags, a new type of visual programming environment (inspired by Snap!) for the teaching of relational operations and data analysis. Students formulate algebraic queries by snapping together graphical blocks that represent data sets and relational operators, resulting in an interactive visualization of the underlying concepts. The outcomes of this work will not only enhance university-level database courses, but also provide an engaging computational thinking resource for K-12 teachers in content areas outside of science and engineering.

No abstract available.

With initiatives like CS Ed Week, Code.org, and the AP CS Principles course, many educators are designing new and engaging ways to introduce students to computer science. But are we doing a good enough job presenting computer science as a field? Is it sufficient for prospective students to understand that CS is about creativity, abstraction, data, algorithms, programming, the Internet, and impact? Or should they also learn something about computer architecture, operating systems, networking and security, programming languages, software engineering, database systems, and so forth? This BOF will discuss how survey courses can meet the learning objectives of AP CS Principles, while providing a wealth of content knowledge that maps directly to undergraduate computer science programs.

Apps4VA is a bold initiative to improve K-12 education by leveraging data on Virginia public school students' progress toward college and career readiness. The goal of the project is for students, professionals, and the general public to create "apps" that explore new ways of using K-12 education data to aid in better data-driven decisions and intervention strategies. In Spring 2013, sixty students in a database systems course participated in a semester-long project based on Apps4VA. This paper describes our teaching experiences, reports our lessons learned working with education data, and documents improvements in student outcomes. We also describe how instructors of database and data analytics courses may partner with local governments to adopt similar projects in their communities.

Computational thinking (CT) is broadly defined as the mental activity for abstracting problems and formulating solutions that can be automated. In an increasingly information-based society, CT is becoming an essential skill for everyone. To ensure that students develop this ability at the K-12 level, it is important to provide teachers with an adequate knowledge about CT and how to incorporate it into their teaching. This article describes a study on designing and introducing computational thinking modules and assessing their impact on preservice teachers’ understanding of CT concepts, as well as their attitude towards computing. Results demonstrate that introducing computational thinking into education courses can effectively influence preservice teachers’ understanding of CT concepts.

As computational thinking becomes a fundamental skill for the 21st century, K-12 teachers should be exposed to computing principles. This paper describes the implementation and evaluation of a computational thinking module in a required course for elementary and secondary education majors. We summarize the results from open-ended and multiple-choice questionnaires given both before and after the module to assess the students' attitudes toward and understanding of computational thinking. The results suggest that given relevant information about computational thinking, education students' attitudes toward computer science becomes more favorable and they will be more likely to integrate computing principles in their future teaching.

Real-world databases often contain syntactic and semantic errors, in spite of integrity constraints and other safety measures incorporated into modern DBMSs. We present ERACER, an iterative statistical framework for inferring missing information and correcting such errors automatically. Our approach is based on belief propagation and relational dependency networks, and includes an efficient approximate inference algorithm that is easily implemented in standard DBMSs using SQL and user defined functions. The system performs the inference and cleansing tasks in an integrated manner, using shrinkage techniques to infer correct values accurately even in the presence of dirty data. We evaluate the proposed methods empirically on multiple synthetic and real-world data sets. The results show that our framework achieves accuracy comparable to a baseline statistical method using Bayesian networks with exact inference. However, our framework has wider applicability than the Bayesian network baseline, due to its ability to reason with complex, cyclic relational dependencies.

The inherent uncertainty of data present in numerous applications such as sensor databases, text annotations, and information retrieval motivate the need to handle imprecise data at the database level. Uncertainty can be at the attribute or tuple level and is present in both continuous and discrete data domains. This paper presents a model for handling arbitrary probabilistic uncertain data (both discrete and continuous) natively at the database level. Our approach leads to a natural and efficient representation for probabilistic data. We develop a model that is consistent with possible worlds semantics and closed under basic relational operators. This is the first model that accurately and efficiently handles both continuous and discrete uncertainty. The model is implemented in a real database system (PostgreSQL) and the effectiveness and efficiency of our approach is validated experimentally.

Orion is a state-of-the-art uncertain database management system with built-in support for probabilistic data as first class data types. In contrast to other uncertain databases, Orion supports both attribute and tuple uncertainty with arbitrary correlations. This enables the database engine to handle both discrete and continuous pdfs in a natural and accurate manner. The underlying model is closed under the basic relational operators and is consistent with Possible Worlds Semantics. We demonstrate how Orion simplifies the design and enhances the capabilities of two example applications: managing sensor data (continuous uncertainty) and inferring missing values (discrete uncertainty).

Uncertainty in categorical data is commonplace in many applications, including data cleaning, database integration, and biological annotation. In such domains, the correct value of an attribute is often unknown, but may be selected from a reasonable number of alternatives. Current database management systems do not provide a convenient means for representing or manipulating this type of uncertainty. In this paper we extend traditional systems to explicitly handle uncertainty in data values. We propose two index structures for efficiently searching uncertain categorical data, one based on the R-tree and another based on an inverted index structure. Using these structures, we provide a detailed description of the probabilistic equality queries they support. Experimental results using real and synthetic datasets demonstrate how these index structures can effectively improve the performance of queries through the use of internal probabilistic information.