13 research outputs found
Handling Tradeoffs between Performance and Query-Result Quality in Data Stream Processing
Data streams in the form of potentially unbounded sequences of tuples arise naturally in a large variety of domains including finance markets, sensor networks, social media, and network traffic management. The increasing number of applications that require processing data streams with high throughput and low latency have promoted the development of data stream processing systems (DSPS). A DSPS processes data streams with continuous queries, which are issued once and return query results to users continuously as new tuples arrive.
For stream-based applications, both the query-execution performance (in terms of, e.g., throughput and end-to-end latency) and the quality of produced query results (in terms of, e.g., accuracy and completeness) are important. However, a DSPS often needs to make tradeoffs between these two requirements, either because of the data imperfection within the streams, or because of the limited computation capacity of the DSPS itself. Performance versus result-quality tradeoffs caused by data imperfection are inevitable, because the quality of the incoming data is beyond the control of a DSPS, whereas tradeoffs caused by system limitations can be alleviated—even erased—by enhancing the DSPS itself.
This dissertation seeks to advance the state of the art on handling the performance versus result-quality tradeoffs in data stream processing caused by the above two aspects of reasons. For tradeoffs caused by data imperfection, this dissertation focuses on the typical data-imperfection problem of stream disorder and proposes the concept of quality-driven disorder handling (QDDH). QDDH enables a DSPS to make flexible and user-configurable tradeoffs between the end-to-end latency and the query-result quality when dealing with stream disorder. Moreover, compared to existing disorder handling approaches, QDDH can significantly reduce the end-to-end latency, and at the same time provide users with desired query-result quality. In this dissertation, a generic buffer-based QDDH framework and three instantiations of the generic framework for distinct query types are presented. For tradeoffs caused by system limitations, this dissertation proposes a system-enhancement approach that combines the row-oriented and the column-oriented data layout and processing techniques in data stream processing to improve the throughput. To fully exploit the potential of such hybrid execution of continuous queries, a static, cost-based query optimizer is introduced. The optimizer works at the operator level and takes the unique property of execution plans of continuous queries—feasibility—into account
Quality-Driven Disorder Handling for M-way Sliding Window Stream Joins
Sliding window join is one of the most important operators for stream
applications. To produce high quality join results, a stream processing system
must deal with the ubiquitous disorder within input streams which is caused by
network delay, asynchronous source clocks, etc. Disorder handling involves an
inevitable tradeoff between the latency and the quality of produced join
results. To meet different requirements of stream applications, it is desirable
to provide a user-configurable result-latency vs. result-quality tradeoff.
Existing disorder handling approaches either do not provide such
configurability, or support only user-specified latency constraints.
In this work, we advocate the idea of quality-driven disorder handling, and
propose a buffer-based disorder handling approach for sliding window joins,
which minimizes sizes of input-sorting buffers, thus the result latency, while
respecting user-specified result-quality requirements. The core of our approach
is an analytical model which directly captures the relationship between sizes
of input buffers and the produced result quality. Our approach is generic. It
supports m-way sliding window joins with arbitrary join conditions. Experiments
on real-world and synthetic datasets show that, compared to the state of the
art, our approach can reduce the result latency incurred by disorder handling
by up to 95% while providing the same level of result quality.Comment: 12 pages, 11 figures, IEEE ICDE 201
On the origin and evolution of RNA editing in metazoans
Extensive adenosine-to-inosine (A-to-I) editing of nuclear-transcribed mRNAs is the hallmark of metazoan transcriptional regulation. Here, by profiling the RNA editomes of 22 species that cover major groups of Holozoa, we provide substantial evidence supporting A-to-I mRNA editing as a regulatory innovation originating in the last common ancestor of extant metazoans. This ancient biochemistry process is preserved in most extant metazoan phyla and primarily targets endogenous double-stranded RNA (dsRNA) formed by evolutionarily young repeats. We also find intermolecular pairing of sense-antisense transcripts as an important mechanism for forming dsRNA substrates for A-to-I editing in some but not all lineages. Likewise, recoding editing is rarely shared across lineages but preferentially targets genes involved in neural and cytoskeleton systems in bilaterians. We conclude that metazoan A-to-I editing might first emerge as a safeguard mechanism against repeat-derived dsRNA and was later co-opted into diverse biological processes due to its mutagenic nature
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
Handling Tradeoffs between Performance and Query-Result Quality in Data Stream Processing
Data streams in the form of potentially unbounded sequences of tuples arise naturally in a large variety of domains including finance markets, sensor networks, social media, and network traffic management. The increasing number of applications that require processing data streams with high throughput and low latency have promoted the development of data stream processing systems (DSPS). A DSPS processes data streams with continuous queries, which are issued once and return query results to users continuously as new tuples arrive.
For stream-based applications, both the query-execution performance (in terms of, e.g., throughput and end-to-end latency) and the quality of produced query results (in terms of, e.g., accuracy and completeness) are important. However, a DSPS often needs to make tradeoffs between these two requirements, either because of the data imperfection within the streams, or because of the limited computation capacity of the DSPS itself. Performance versus result-quality tradeoffs caused by data imperfection are inevitable, because the quality of the incoming data is beyond the control of a DSPS, whereas tradeoffs caused by system limitations can be alleviated—even erased—by enhancing the DSPS itself.
This dissertation seeks to advance the state of the art on handling the performance versus result-quality tradeoffs in data stream processing caused by the above two aspects of reasons. For tradeoffs caused by data imperfection, this dissertation focuses on the typical data-imperfection problem of stream disorder and proposes the concept of quality-driven disorder handling (QDDH). QDDH enables a DSPS to make flexible and user-configurable tradeoffs between the end-to-end latency and the query-result quality when dealing with stream disorder. Moreover, compared to existing disorder handling approaches, QDDH can significantly reduce the end-to-end latency, and at the same time provide users with desired query-result quality. In this dissertation, a generic buffer-based QDDH framework and three instantiations of the generic framework for distinct query types are presented. For tradeoffs caused by system limitations, this dissertation proposes a system-enhancement approach that combines the row-oriented and the column-oriented data layout and processing techniques in data stream processing to improve the throughput. To fully exploit the potential of such hybrid execution of continuous queries, a static, cost-based query optimizer is introduced. The optimizer works at the operator level and takes the unique property of execution plans of continuous queries—feasibility—into account
Handling Tradeoffs between Performance and Query-Result Quality in Data Stream Processing
Data streams in the form of potentially unbounded sequences of tuples arise naturally in a large variety of domains including finance markets, sensor networks, social media, and network traffic management. The increasing number of applications that require processing data streams with high throughput and low latency have promoted the development of data stream processing systems (DSPS). A DSPS processes data streams with continuous queries, which are issued once and return query results to users continuously as new tuples arrive.
For stream-based applications, both the query-execution performance (in terms of, e.g., throughput and end-to-end latency) and the quality of produced query results (in terms of, e.g., accuracy and completeness) are important. However, a DSPS often needs to make tradeoffs between these two requirements, either because of the data imperfection within the streams, or because of the limited computation capacity of the DSPS itself. Performance versus result-quality tradeoffs caused by data imperfection are inevitable, because the quality of the incoming data is beyond the control of a DSPS, whereas tradeoffs caused by system limitations can be alleviated—even erased—by enhancing the DSPS itself.
This dissertation seeks to advance the state of the art on handling the performance versus result-quality tradeoffs in data stream processing caused by the above two aspects of reasons. For tradeoffs caused by data imperfection, this dissertation focuses on the typical data-imperfection problem of stream disorder and proposes the concept of quality-driven disorder handling (QDDH). QDDH enables a DSPS to make flexible and user-configurable tradeoffs between the end-to-end latency and the query-result quality when dealing with stream disorder. Moreover, compared to existing disorder handling approaches, QDDH can significantly reduce the end-to-end latency, and at the same time provide users with desired query-result quality. In this dissertation, a generic buffer-based QDDH framework and three instantiations of the generic framework for distinct query types are presented. For tradeoffs caused by system limitations, this dissertation proposes a system-enhancement approach that combines the row-oriented and the column-oriented data layout and processing techniques in data stream processing to improve the throughput. To fully exploit the potential of such hybrid execution of continuous queries, a static, cost-based query optimizer is introduced. The optimizer works at the operator level and takes the unique property of execution plans of continuous queries—feasibility—into account
Piezoelectric Resonators Excited by Lateral Electric Fields Based on a LiTaO3 Single Crystal
In the present study, piezoelectric resonators under lateral field excitation (LFE) based on a LiTaO3 single crystal are modeled and analyzed. An electrically forced vibration study is employed to acquire the motional capacitance curve and vibration mode shapes. A finite element approach is utilized to investigate the influences of some basic parameters, such as the electrode/plate mass ratio, electrode gap, and electrode width on resonance characteristics. In addition, the design criteria for the gap and width of the electrode of the LiTaO3 LFE resonators are obtained by analyzing the effects of those parameters on vibration strain distributions. The obtained results are essential for designing LFE piezoelectric resonators by using a LiTaO3 single crystal
Experimental Realization of Two Qutrits Gate with Tunable Coupling in Superconducting Circuits
Gate-based quantum computation has been extensively investigated using
quantum circuits based on qubits. In many cases, such qubits are actually made
out of multilevel systems but with only two states being used for computational
purpose. While such a strategy has the advantage of being in line with the
common binary logic, it in some sense wastes the ready-for-use resources in the
large Hilbert space of these intrinsic multi-dimensional systems. Quantum
computation beyond qubits (e.g., using qutrits or qudits) has thus been
discussed and argued to be more efficient than its qubit counterpart in certain
scenarios. However, one of the essential elements for qutrit-based quantum
computation, two-qutrit quantum gate, remains a major challenge. In this work,
we propose and demonstrate a highly efficient and scalable two-qutrit quantum
gate in superconducting quantum circuits. Using a tunable coupler to control
the cross-Kerr coupling between two qutrits, our scheme realizes a two-qutrit
conditional phase gate with fidelity 89.3% by combining simple pulses applied
to the coupler with single-qutrit operations. We further use such a two-qutrit
gate to prepare an EPR state of two qutrits with a fidelity of 95.5%. Our
scheme takes advantage of a tunable qutrit-qutrit coupling with a large on/off
ratio. It therefore offers both high efficiency and low cross talk between
qutrits, thus being friendly for scaling up. Our work constitutes an important
step towards scalable qutrit-based quantum computation.Comment: 12 pages, 8 figure