This paper introduces LakeHarbor, a new data management paradigm that makes structures (e.g., indexes) first-class citizens in data lakes. The LakeHarbor paradigm enables a data lake system to flexibly construct structures based on registered access method functions and execute data processing jobs efficiently with the potential parallelism that the structures inherently hold by exploiting the functions while not sacrificing flexible data processing such as schema-on-read. This paper also presents ReDe, a prototype data processing engine that implements LakeHarbor, and a motivating evaluation and a case study of ReDe to explore the potential of LakeHarbor.

Hash joins and sort-merge joins have been considered the algorithms of choice for analytical relational queries in most parallel database systems because of their performance robustness and ease of parallelization. On the other hand, nested loop joins have been considered less attractive and are conservatively used. In this paper, we revisit the potential of nested loop joins in a cluster environment. We focus on exploring the parallelism aspect of nested loop joins because there could still be space for improvement by fully exploiting the parallelism of current commodity hardware, which could handle more than thousands of concurrent IOs. We also introduce scalable massively-parallel execution as one of the approaches for achieving massive parallelism in nested loop joins to explore how it widens the potential benefit of nested loop joins. Finally, we discuss future research directions based on our exploration.

To realize an autonomous control system that operates in cooperation with humans, safety design that considers human decisions and behaviors is essential. Systems- Theoretic Accident Model and Processes (STAMP) and System- Theoretic Process Analysis (STPA) can analyze interactions between systems and humans that cause hazards, such as mistakes by human operators. However, it is hard to extract the behaviors of humans who are out in the same field as autonomous devices and work in cooperation with the systems as hazard causal factors with conventional STPA methods. In this study, we propose an extended STPA method that can systematically analyze hazard causal factors in the behaviors of humans who coexist and cooperate with autonomous control systems in the same field as autonomous devices. The proposed method (1) defines information that is given to humans by the systems and may cause hazardous human behavior as unsafe control actions and (2) identifies hazard causal factors with guidewords improved to be applicable to humans. Through a case study on a system that controls automated guided vehicles in an area where humans are present, it was shown that the proposed method can extract human's erroneous decisions and behaviors that may cause hazardous situations as hazard causal factors.

This paper presents ScalarDB, a universal transaction manager that achieves distributed transactions across multiple disparate databases. ScalarDB provides a database-agnostic transaction manager on top of its database abstraction; thus, it achieves transactions spanning various databases without depending on the transactional capability of underlying databases. ScalarDB is based on several research works and extended to provide a strong correctness guarantee (i.e., strict serializability), further performance optimizations, and several critical mechanisms for productization. In this paper, we describe the design and implementation of ScalarDB. We also present evaluation results showing that ScalarDB achieves database-spanning transactions with reasonable performance and near-linear scalability without sacrificing correctness. Finally, we share some case studies and lessons learned while building and running ScalarDB.

Collaborative work between workers and autonomous systems in the same area is required to improve operation efficiency. However, there exist collision risks caused by coexistence of workers and autonomous systems. The safety functions of the autonomous systems, such as emergency stops, can reduce the risks and but may decrease the operation efficiency. Therefore, we propose a novel safety concept called Symbiotic Safety. The concept improves both safety and operation efficiency by transformation of action plan, e.g., adjustment of action plan or update of safety rule, which reduces frequency of risk occurrence and suppress efficiency loss due to safety functions. In this paper, we explain the symbiotic safety technologies and share results of an evaluation experiment by utilizing our prototype system.

This paper presents Scalar DL, a Byzantine fault detection (BFD) middleware for transactional database systems. Scalar DL manages two separately administered database replicas in a database system and can detect Byzantine faults in the database system as long as either replica is honest (not faulty). Unlike previous BFD works, Scalar DL executes non-conflicting transactions in parallel while preserving a correctness guarantee. Moreover, Scalar DL is database-agnostic middleware so that it achieves the detection capability in a database system without either modifying the databases or using database-specific mechanisms. Experimental results with YCSB and TPC-C show that Scalar DL outperforms a state-of-the-art BFD system by 3.5 to 10.6 times in throughput and works effectively on multiple database implementations. We also show that Scalar DL achieves near-linear (91%) scalability when the number of nodes composing each replica increases.

Intra-query parallelism is a key for database software to offer acceptable responsiveness for data-intensive queries. Many researchers have studied how to achieve greater execution parallelism for database queries. Partitioning is a representative approach, which divides a query into multiple sub-tasks and executes them in parallel. However, given a new query, optimal division is not necessarily obvious. Database software utilizes heuristic rules or statistical information to decide how to divide the query before execution. As yet another approach to achieve execution parallelism, this paper presents out-of-order database execution (OoODE), a massively-parallel query execution method to offer significant speedup for database queries consistently. OoODE dynamically decomposes query work by making the best use of the exact knowledge of the potential execution parallelism for each operation ready to be performed during query execution. With OoODE, the database software is allowed to automatically squeeze out the execution parallelism that the query inherently holds. Hence, for a wide spectrum of queries, OoODE performs significantly faster than the serial (non-parallelized) execution, while it performs better than or comparably with alternative parallelizing methods without the need for dividing the query before execution. This paper presents the experiments that we conducted using the prototyped database software and demonstrates that OoODE is two to three orders of magnitude faster than the serial execution, whereas it is substantially (up to 2.07 times) faster than the best achievable case of partitioning. Besides, OoODE performs two to four orders of magnitude faster than major DBMSs.

The Networked Improvement Learning and Support (NILS) platform is an online tool designed to accelerate the initiation and development of Networked Improvement Communities in a disciplined manner. Its main goal is to promote social, organizational learning through curation and synthesis and tacit to explicit knowledge conversion to facilitate knowledge construction and ownership by the communities regarding improvement practice in education. In this proposal we will discuss the NILS platform, a few use cases, and a plan of analytics development that advances knowledge dissemination and monitors the health status of networks.

Statway® is one of the Community College Pathways initiatives designed to promote students' success in their developmental math sequence and reduce the time required to earn college credit. A recent causal analysis confirmed that Statway dramatically increased students' success rates in half the time across two different cohorts. These impressive results were also obtained across gender and race/ethnicity groups. However, there is still room for improvement. Students who did not succeed in Statway often did not complete the first of the two-course sequence. Therefore, the objective of this study is to formulate a series of indicators from self-report and online learning system data, alerting instructors to students' progress during the first weeks of the first course in the Statway sequence.

This paper argues that improving learning reliably and at scale requires a specific orientation toward measurement, understood broadly. Drawing on examples from a partnership between SRI International and The Carnegie Foundation for the Advancement of Teaching, this paper describes measures of student behaviors that are being used by researchers and instructors to improve learning environments at more than 50 community colleges and four-year universities for thousands of students.

This paper presents a sensor network system consisting of distributed cameras and laser range finders for multiple objects tracking. Sensory information from cameras is processed by the level set method in real time and integrated with range data obtained by laser range finders in a probabilistic manner using novel SIR/MCMC combined particle filters. Though the conventional SIR particle filter is a popular technique for object tracking, it has been pointed out that the conventional particle filter has some disadvantages in practical applications such as its low tracking performance for multiple targets due to the degeneracy problem. In this paper, the new combined particle filters consisting of a low-resolution MCMC particle filter and a high-resolution SIR particle filter is proposed. Simultaneous tracking experiments for multiple moving targets are successfully carried out and it is verified that the combined particle filters has higher performance than the conventional particle filters in terms of the number of particles, the processing speed, and the tracking performance for multiple targets.

This paper proposes a session control method which provides a mechanism to achieve desirable user-level QoS (i.e., perceptual QoS) of audio-video transmission over IP networks. The proposed method, which is referred to as GPSQ (Guarantee of Psychologically Scaled Quality), utilizes a user-level QoS parameter of the interval scale in the psychometric analysis. GPSQ involves four kinds of components: media terminals, a SIP server, a QoS manager and bandwidth-controllable routers. From the media terminals, the SIP server obtains the information for this control, which is further delivered to the QoS manager. The QoS manager keeps a database of representative regression lines which express user-level QoS as a function of guaranteed bandwidth of audio and that of video. Using the regression line, the QoS manager calculates necessary guaranteed bandwidth according to user-level QoS specified by the user. Then, the QoS manager sets up routers to guarantee the calculated bandwidth. We implemented GPSQ in a simple experimental network and confirmed its effectiveness.

The present paper describes a two-degree-of-freedom control of a self-sensing micro-actuator for a dual-stage hard disk drive using neural networks. The two-degree-of-freedom control system is comprised of a feedforward controller and a feedback controller. Two neural networks are developed for the two-degree-of-freedom control system, one for the inverse dynamic model for the feedforward controller and one for system identification for the generation of the desired self-sensing signal. The feedback controller can realize the identified self-sensing signal. The micro-actuator uses a PZT actuator pair, installed on the assembly of the suspension. The self-sensing micro-actuator can be used to form a combined actuation and sensing mechanism. Experimental results show that the neural network approach can be used effectively for the control and identification of the self-sensing micro-actuator system

To reduce the test application time and the test data volume in full-scan testing, various methods are proposed which utilize some additional built-in circuits dedicated for testing. In contrast, a previous method, called Reduced Scan Shift, does not utilize any additional built-in hardware. However, the method relies on scan chain flip-flop reordering, which is not always applicable. In this paper, we propose a test data sequence generation method for Reduced Scan Shift without scan chain flip-flop reordering. Our method fully utilizes justification technique and don't-care bits in test vectors.