The Wrong Kind of Lean: Over-Commitment and Under-Represented Skills on Technology Teams

This paper reports on results from five companies in the aerospace and automotive industries to show that over-commitment of technical professionals and under-representation of key skills on technology development and transition teams seriously impairs team performance. The research finds that 40 percent of the projects studied were inadequately staffed, resulting in weaker team communications and alignment. Most importantly, the weak staffing on these teams is found to be associated with a doubling of project failure rate to reach full production. Those weakly staffed teams that did successfully insert technology into production systems were also much more likely than other teams to have development delays and late engineering changes. The conclusion suggests that the expense of project failure, delay and late engineering changes in these companies must greatly out-weigh the savings gained from reduced staffing costs, and that this problem is likely going to be found in other technology-intensive firms intent on seeing project budgets as a cost to be minimized rather than an investment to be maximized.

Bit-Width Analysis for General Applications

It has been widely known that a significant part of the bits are useless or even unused during the program execution. Bit-width analysis targets at finding the minimum bits needed for each variable in the program, which ensures the execution correctness and resources saving. In this paper, we proposed a static analysis method for bit-widths in general applications, which approximates conservatively at compile time and is independent of runtime conditions. While most related work focus on integer applications, our method is also tailored and applicable to floating point variables, which could be extended to transform floating point number into fixed point numbers together with precision analysis. We used more precise representations for data value ranges of both scalar and array variables. Element level analysis is carried out for arrays. We also suggested an alternative for the standard fixed-point iterations in bi-directional range analysis. These techniques are implemented on the Trimaran compiler structure and tested on a set of benchmarks to show the results.

Exploring Bit-Difference for Approximate KNN Search in High-dimensional Databases

In this paper, we develop a novel index structure to support efficient approximate k-nearest neighbor (KNN) query in high-dimensional databases. In high-dimensional spaces, the computational cost of the distance (e.g., Euclidean distance) between two points contributes a dominant portion of the overall query response time for memory processing. To reduce the distance computation, we first propose a structure (BID) using BIt-Difference to answer approximate KNN query. The BID employs one bit to represent each feature vector of point and the number of bit-difference is used to prune the further points. To facilitate real dataset which is typically skewed, we enhance the BID mechanism with clustering, cluster adapted bitcoder and dimensional weight, named the BID⁺. Extensive experiments are conducted to show that our proposed method yields significant performance advantages over the existing index structures on both real life and synthetic high-dimensional datasets.