Dow Jones Industrial Average

The Dow Jones Industrial Average (DJIA), Dow Jones, or simply the Dow ('daʊ), is a stock market index of 30 prominent companies listed on stock exchanges in the United States. The DJIA is one of the oldest and most commonly followed equity indexes. Many professionals consider it to be an inadequate representation of the overall U.S. stock market compared to a broader market index such as the S&P 500. The DJIA includes only 30 large companies. It is price-weighted, unlike stock indices, which use market capitalization. Furthermore, the DJIA does not use a weighted arithmetic mean. The value of the index can also be calculated as the sum of the stock prices of the companies included in the index, divided by a factor, which is approximately 0.152 . The factor is changed whenever a constituent company undergoes a stock split so that the value of the index is unaffected by the stock split. First calculated on May 26, 1896, the index is the second-oldest among U.

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Semantically Enriched Industry Data & Information Modelling: A feasibility study on Shop-floor Incident Recognition

Damiano Nunzio Arena, Dimitrios Kyritsis, Apostolos Perdikakis

Knowledge modelling at industrial level consists an importunate activity nowadays due to the ceaseless advances in technologies and standards applied as well as the extensive amount of unrelated real-time and historical data at shop-floor level. A Common Interface Data Exchange Model (CIDEM) is hereby introduced towards unifying continuously produced data from heterogeneous and distributed information sources - on different levels and granularities - into a shared vocabulary that can unobtrusively communicate with industrial standards and protocols (i.e. B2MML, gbXML, MIMOSA). For further enhancing the information model a conceptual definition is employed leading to a semantically enriched model which enables more understandable high level knowledge diffusion. This model has been applied to various industrial applications, one of the most important being industrial safety, through incident recognition.

IEEE2016

Interleaving with Coroutines: A Practical Approach for Robust Index Joins

Anastasia Ailamaki, Georgios Psaropoulos

Index join performance is determined by the efficiency of the lookup operation on the involved index. Although database indexes are highly optimized to leverage processor caches, main memory accesses inevitably increase lookup runtime when the index outsizes the last-level cache; hence, index join performance drops. Still, robust index join performance becomes possible with instruction stream interleaving: given a group of lookups, we can hide cache misses in one lookup with instructions from other lookups by switching among their respective instruction streams upon a cache miss. In this paper, we propose interleaving with coroutines for any type of index join. We showcase our proposal on SAP HANA by implementing binary search and CSB+-tree traversal for an instance of index join related to dictionary compression. Coroutine implementations not only perform similarly to prior interleaving techniques, but also resemble the original code closely, while supporting both interleaved and non-interleaved execution. Thus, we claim that coroutines make interleaving practical for use in real DBMS codebases.

VLDB Endowment Inc.2017

Emergence of diauxie as an optimal growth strategy under resource allocation constraints in cellular metabolism

Vassily Hatzimanikatis, Pierre Guy Rémy Salvy

Diauxie, or the sequential consumption of carbohydrates in bacteria such as Escherichia coli, has been hypothesized to be an evolutionary strategy which allows the organism to maximize its instantaneous specific growth-giving the bacterium a competitive advantage. Currently, the computational techniques used in industrial biotechnology fall short of explaining the intracellular dynamics underlying diauxic behavior. In particular, the understanding of the proteome dynamics in diauxie can be improved. We developed a robust iterative dynamic method based on expression- and thermodynamically enabled flux models to simulate the kinetic evolution of carbohydrate consumption and cellular growth. With minimal modeling assumptions, we couple kinetic uptakes, gene expression, and metabolic networks, at the genome scale, to produce dynamic simulations of cell cultures. The method successfully predicts the preferential uptake of glucose over lactose in E. coli cultures grown on a mixture of carbohydrates, a manifestation of diauxie. The simulated cellular states also show the reprogramming in the content of the proteome in response to fluctuations in the availability of carbon sources, and it captures the associated time lag during the diauxie phenotype. Our models suggest that the diauxic behavior of cells is the result of the evolutionary objective of maximization of the specific growth of the cell. We propose that genetic regulatory networks, such as the lac operon in E. coli, are the biological implementation of a robust control system to ensure optimal growth.

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