Mixed-norm sparse representation for multi view face recognition Face recognition with multiple views is a challenging research problem. Most of existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation is too large or missing, 'shared information' may not be properly extracted, leading to poor recognition results. In this paper, we propose a novel method for face recognition with multiple view images to overcome the large pose variation and missing pose issue. By introducing a novel mixed norm, the proposed method automatically selects candidates from the gallery to best represent a group of highly correlated face images in a query set to improve classification accuracy. This mixed norm combines the advantages of both sparse representation based classification (SRC) and joint sparse representation based classification (JSRC). A trade off between the ℓ 1 -norm from SRC and ℓ 2,1 -norm from JSRC is introduced to achieve this goal. Due to this property, the proposed method decreases the influence when a face image is unseen and has large pose variation in the recognition process. And when some face images with a certain degree of unseen pose variation appear, thisMarch 12, 2015 mixed norm will find an optimal representation for these query images based on the shared information induced from multiple views. Moreover, we also address an open problem in robust sparse representation and classification which is using ℓ 1 -norm on the loss function to achieve a robust solution. To solve this formulation, we derive a simple, yet provably convergent algorithm based on the powerful alternative directions method of multipliers (ADMM) framework. We provide extensive comparisons which demonstrate that our method outperforms other state-of-the-arts algorithms on CMU-PIE, Yale B and Multi-PIE databases for multi-view face recognition. Face recognition is one of non-intrusive biometrics. Due to the emerging demand in surveillance and security, it is an important research topic in pattern recognition [1] .. According to previous literature surveys [2, 3] , extensive studies have been done to resolve the face recognition issues, such as pose, illumination, expression and occlusion, etc. [4, 5, 6] . However, most of these methods are based on a single input image. They identify a subject by matching a single query face image with all gallery images one by one. In practice, it is common that the query face image is noisy or its pose may be missing in the gallery, thus working with a single face image is likely to be unreliable in real-world applications. On the other hand, multiple views of a same subject can be obtained easily with current technology. For instance, a sequence of face images from a subject with a large degree of pose variations may be observed over a time interval by a surveillance camera or multiple snapshots are captured by video camera networks at same time from different viewpoints. This will produce a large number of query images for recognition tasks. Since multiple view images are from the same subject under different time or viewpoint, there is likely some shared information across those face images. The existing face recognition techniques have not investigated the inter-correlation among the query images, therefore, exploiting the using of these shared information becomes an important work.. In the face recognition literature, several popular classifiers have been developed. The nearest neighbour (NN) is one of the most common and popular classifiers [7] . The NN classifies the query face image based on its closest neighbour in the gallery set. However, this classifier is sensitive to outliers. The NN classifier is generalized to nearest subspace (NS) [8] . Instead using a single image to perform classification, NS classifies a face based on the best linear representation in terms of all the gallery images in each class.. Since the classification decision is made by all samples, NS is more robust than NN. Sparse representation-based classification (SRC) [6] seeks a balance between these two extreme cases, it represents a query image by adaptively choosing a minimum number of atoms (samples in gallery) from both within each class and across multiple classes. SRC has been shown more robust and effective than NN and NS on some common face recognition issues, such as occlusion and corruption. Encouraged by the SRC framework, a large number of its extensions have been proposed [9, 10, 11] and they have achieved stateof-art performance. However, they are limited to single query face image for recognition.. Recently, a growing interest [12, 13, 14, 15] in face recognition from an image set has emerged. Rather than using a single query image to perform recognition, multiple face images of the same subject are used as an input. In general, the system identifies a query subject based on a set of input images from known subjects in the gallery. The face images in both gallery and query sets may have large variations in pose, illumination, etc. By using multiple face images of the same subject in the query, the robustness of the recognition system has been improved significantly compared with single-input systems.. In [16] , an extended volume Local Binary Patterns is introduced to exploit the information among frames. It can achieve a good performance, but it requires sequential images from a video. Another approach to achieve this goal is by measuring the distance between the query set and each class in the gallery set.. Inspired by label propagation [17] , a graph-based classification for multiple view face recognition has been proposed [12] . It converts the face image set into a similarity graph, and then uses a class-wise graph matching procedure to compare this similarity graph with the graph generated by each class in the gallery. In [13] , face images are represented as a feature vector in an affine feature space. They build an affine hull for each image set (query set and each class in the gallery). The geometric distances between the affine hull of query and of each class in the gallery are used to make the classification decision. A multi-class group Lasso is introduced in [18] . Images are represented by Local Binary Patterns [19] , then the best suitable features are selected to measure the distance between each pair of sets. These methods treat each set of the gallery face images as a linear subspace, and use subspace distance to identify the query subject from subjects in the gallery independently. Thus, they have two limitations: (1) they cannot exploit information across multiple classes;. (2) when there is a large difference between images in the same class, such as large pose variations, these methods can perform poorly.. Since SRC considers both within each class and between multiple class factors, a multiple test samples generalization of SRC is introduced, known as joint sparse representation-based classification (JSRC) [14, 20] . This method assumes that the query face images share the same sparsity pattern. The shared information can be exploited by using this assumption. Instead of solving the SRC problem for each query image, JSRC solves a set of query images from the same subject. It adaptively selects a minimum number of atoms from gallery images, these atoms can best represent every query images at same time. However, this assumption will not hold when there are large pose differences in the query images. For example, if a frontal face and a In this paper, we have proposed a mixed-norm sparse representation classification, which has been demonstrated to outperform rivals. This is due to the advantage of exploiting the inter-correlation among the multiple face images in the query, the flexibility of atom selection and the robustness brought by an ℓ 1 -loss function. Furthermore, this MSRC is built on the powerful ADMM framework, which results in a very simple, yet provably convergent, algorithm, where further improvement in both performance and computation can be made. We have demonstrated the power of the ADMM framework in deriving numerical algorithm to solve the proposed formulation. We also In this section, we present extensive experiments on CMU-PIE [31] PCA is used to resize the images to suitable working dimensions.. Whilst the main method for comparison is JDSRC outlined in [21] , we also include the original SRC [6] and JSRC [14, 20] . In addition, some popular base line face recognition techniques are also evaluated, including principal component analysis (PCA) and linear discriminant analysis (LDA) [4] . Since SRC is originally used for single task, we follow [15] to use majority voting for SRC classification step and do the same for JSRC [20] . For notational convenience, we denote the mixed-norm sparse representation classification as MSRC. In this work, we follow the standard cross validation procedure in machine learning to select the regularization parameter λ for SRC and JSRC. This is achieved by further dividing the training set into a smaller training set and a validation set. For the proposed MSRC method, we also follow the same procedure, wherein all the training, validation, and test sets are exactly the same as those used for SRC and JSRC. The only minor differ- ence is that MSRC has both the regularization parameter λ and the mixed norm parameter γ (ranged between 0 and 1). This means the computation slightly increases because the search is done on two dimensions. From the interpretation of the role of γ in controlling the pose variation and our intensive numerical studies, we suggest that the computational increase in cross validation due to γ might be reduced by a preliminary estimation of the pose variation. We notice that when there is large pose variation in the test set, a larger γ is preferred and vice versa. This suggests that if we use a reliable pose detection method such as [34] , we may have a good estimate of the pose variation and hence a fixed γ can be set without sacrificing an increase in computation due to cross validation. However, we shall address this issue in a great depth in future works.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Absalom:

Balancing

assembly

lines

with

assignment

restrictions

Assembly

line

balancing

problems

(ALBPs)

arise

whenever

an

assembly

line

is

configured,

redesigned

or

adjusted.

An

ALBP

consists

of

distributing

the

total

workload

for

manufacturing

products

among

the

work

stations

along

the

line.

On

the

one

hand,

research

has

focussed

on

developing

effective

and

fast

solution

methods

for

exactly

solving

the

simple

assembly

line

balancing

problem

(SALBP).

On

the

other

hand,

a

number

of

real-world

extensions

of

SALBP

have

been

introduced

but

solved

with

straightforward

and

simple

heuristics

in

many

cases.

Therefore,

there

is

a

lack

of

procedures

for

exactly

solving

such

generalized

ALBP.In

this

paper,

we

show

how

to

extend

the

well-known

solution

procedure

SALOME

(INFORMS

J.

Computing

9,(319)(320)(321)(322)(323)(324)(325)(326)(327)(328)(329)(330)(331)(332)(333)(334),

which

is

able

to

solve

even

large

SALBP

instances

in

a

very

effective

manner,

to

a

problem

extension

with

different

types

of

assignment

restrictions

(called

AR-ALBP).

The

extended

procedure

is

given

the

acronym

ABSALOM.

It

consists

of

a

favourable

branching

scheme,

an

arsenal

of

bounding

rules

and

a

variety

of

logical

tests

using

ideas

from

constraint

programming.Computational

experiments

show

that

ABSALOM

is

a

very

promising

exact

solution

approach

though

the

additional

assignment

restrictions

complicate

the

problem

and

require

some

components

of

SALOME

to

be

modified

in

a

relaxing

manner.

O O O O O B I B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O


Security Ontology for Adaptive Mapping of Security Standards Adoption of security standards has the capability of improving the security level in an organization as well as to provide additional benefits and possibilities to the organization. However mapping of used standards has to be done when more than one security standard is employed in order to prevent redundant activities, not optimal resource management and unnecessary outlays. Employment of security ontology to map different standards can reduce the mapping complexity however the choice of security ontology is of high importance and there are no analyses on security ontology suitability for adaptive standards mapping. In this paper we analyze existing security ontologies by comparing their general properties, OntoMetric factors and ability to cover different security standards. As none of the analysed security ontologies were able to cover more than 1/3 of security standards, we proposed a new security ontology, which increased coverage of security standards compared to the existing ontologies and has a better branching and depth properties for ontology visualization purposes. During this research we mapped 4 security standards (ISO 27001, PCI DSS, ISSA 5173 and NISTIR 7621) to the new security ontology, therefore this ontology and mapping data can be used for adaptive mapping of any set of these security standards to optimize usage of multiple security standards in an organization. An ontology defines the basic terms and relations compromising the vocabulary of a topic area as well as the rules for combining terms and relations to define extensions to the vocabulary [9] . The ontology provides a better communication, reusability and organization of knowledge by decreasing language ambiguity and structuring transferred data [1] , [2] , [3] , [4] .. Security becomes fundamental in our society and the survival of organizations depends on the correct management of up-to-date security elements [6] . As the security area is very broad and has many relations between its concepts, usage of security ontology could improve security knowledge description unambiguity in information systems. The necessity of security ontology can be noticed in various security communities and considered as an important challenge and a research branch [5] , [7] , [8] .. In small and medium enterprises the knowledge database of security area and its unambiguity is very important in formal/legal activities, such as certification, standard compliance, etc. In many cases organizations have to meet certain security requirements from different sources, which may be redundant or overlapping by simultaneous usage. Therefore standard mapping should be put into practice in cases where more than one security standard has to be met. The mapping of security standards allows the optimization of resources by indicating matching elements of standards and by eliminating duplicated activities and security measures to meet it. However mapping of security standards can be complicated if more than two standards have to be mapped. Security ontology can be used to simplify the mapping of more than two security standards [10] . This solution suggests mapping all security standards under one security ontology. This ontology would act as a basis for standard knowledge formalization and would allow adaptive mapping of any standards, mapped to the ontology. Therefore the security ontology plays a large role in adaptive mapping and covers a wide area and should be detailed to meet all security standards.. The aim of this paper is to analyse suitability of existing security ontologies to be used for adaptive mapping of security standards and to propose a new one, more suitable for the purpose.

B I O B I O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O B I O O B I O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O


Expert Stock Picker: The Wisdom of (Experts in) Crowds The phrase "the wisdom of crowds" suggests that good verdicts can be achieved by averaging the opinions and insights of large, diverse groups of people who possess varied types of information. Online user-generated content enables researchers to view the opinions of large numbers of users publicly. These opinions, in the form of reviews and votes, can be used to automatically generate remarkably accurate verdicts-collective estimations of future performance-about companies, products, and people on the Web to resolve very tough problems. The wealth and richness of user-generated content may enable firms and individuals to aggregate consumer-think for better business understanding. Our main contribution, here applied to user-generated stock pick votes from a widely used online financial newsletter, is a genetic algorithm approach that can be used to identify the appropriate vote weights for users based on their prior individual voting success. Our method allows us to identify and rank "experts" within the crowd, enabling better stock pick decisions than the S&P 500. We show that the online crowd performs better, on average, than the S&P 500 for two test time periods, 2008 and 2009, in terms of both overall returns and risk-adjusted returns, as measured by the Sharpe ratio. Furthermore, we show that giving more weight to the votes of the experts in the crowds increases the accuracy of the verdicts, yielding an even greater return in the same time periods. We test our approach by utilizing more than three years of publicly available stock pick data. We compare our method to approaches derived from both the computer science and finance literature. We believe that our approach can be generalized to other domains where user opinions are publicly available early and where those opinions can be evaluated. For example, YouTube video ratings may be used to predict downloads, or online reviewer ratings on Digg may be used to predict the success or popularity of a story.Keywords data mining, prediction markets, social media, user-generated content, wisdom of crowds Expert Stock Picker: The Wisdom of (Experts in) CrowdsABSTRACT: The phrase "the wisdom of crowds" suggests that good verdicts can be achieved by averaging the opinions and insights of large, diverse groups of people who possess varied types of information. Online user-generated content enables researchers to view the opinions of large numbers of users publicly. These opinions, in the form of reviews and votes, can be used to automatically generate remarkably accurate verdicts-collective estimations of future performance-about companies, products, and people on the Web to resolve very tough problems. The wealth and richness of user-generated content may enable firms and individuals to aggregate consumer-think for better business understanding. Our main contribution, here applied to user-generated stock pick votes from a widely used online financial newsletter, is a genetic algorithm approach that can be used to identify the appropriate vote weights for users based on their prior individual voting success. Our method allows us to identify and rank "experts" within the crowd, enabling better stock pick decisions than the S&P 500. We show that the online crowd performs better, on average, than the S&P 500 for two test time periods, 2008 and 2009, in terms of both overall returns and risk-adjusted returns, as measured by the Sharpe ratio. Furthermore, we show that giving more weight to the votes of the experts in the crowds increases the accuracy of the verdicts, yielding an even greater return in the same time periods. We test our approach by utilizing more than three years of publicly available stock pick data. We compare our method to approaches derived from both the computer science and finance literature. We believe that our approach can be generalized to other domains where user opinions are publicly available early and where those opinions can be evaluated. For example, YouTube video ratings may be used to predict downloads, or online reviewer ratings on Digg may be used to predict the success or popularity of a story. As is to be expected, in many ways this research prompts more questions than it answers. Most critically, will the results stand up with more data? Although every effort has been made to validate these results, their weakest point is the short time period in which they were created. Because testing stopped in December 2009, there are now additional data with which to further test the GA's results. What would be the effect of some type of rolling optimization? For example, what if, rather than using the training period to create one canonical set of parameter values, the system instead trained over months 1 and 2, and then used the results of the training in month 3? There is some evidence that the closer temporal proximity between the training and testing periods results in better returns, and this technique potentially takes advantage of this effect.. Would weighting the positions improve returns? It reasonably may be assumed that the algorithm is more "sure" that the first stock is a better long bet than the fifteenth stock, although both positions will be taken. Thus, it may make more sense to place a greater fraction of the portfolio on the 1st stock's position. Is momentum in the number of picks at all significant? Although the total number of picks from day to day does not change rapidly, the number of picks on any given stock does vary substantially. It is possible that there are additional data contained in this behavior that the strategy may be able to take advantage of. Naturally, it would be advantageous to consider incorporating other variables, such as stock fundamentals or price momentum, as well. We plan to incorporate traditional variables in future work. We also plan to apply this approach to other data sets (Digg and YouTube) to predict hits. Finally, we cannot ignore the behavioral aspects of this study-we need a better understanding of what motivates people to use the Motley Fool CAPS site and try to get the picks right. In addition, we will consider that the votes of one user may influence the votes of others. The votes not only may be used as online word of mouth [4] but also may influence [23] trust in a particular stock. In this research, when testing our first hypothesis that using the stock picks of a large sample of online users from Motley Fool CAPS enables us to outperform the S&P 500, we find that letting large samples of users selected from the entire crowd vote often outperforms the S&P 500 in our test data. For example, in the second half of 2008, the overall crowd return was -23.2 percent compared to -35.5 percent for the S&p500. We find similar results for 2009. When we tested our second hypothesis, that our expert ranking algorithm will enable us to find better stock portfolios than the S&P 500 and entire crowd, however, we found that our approach for ranking experts in online user generated stock votes helped to identify better stock portfolios than the S&P500 as well as the portfolios based on the entire crowds picks 100 percent of the time.. The punch line: the "wisdom of crowds" seems to be proven in this data set; however, identifying a subset of experts in the crowd enables us to perform much better. Our approach to rank individuals to identify the few experts to use for prediction is what separates our work from that explained in a working paper we found that considers a very similar data set sourced from CAPS [3] . In our work, we focus on picking the best subset of voters as opposed to only analyzing what happens when the entire crowd votes and the aggregated proprietary star ratings of stocks from CAPS are used. The star ratings indicate stocks that voters in aggregate have picked to outperform the market. In prior work, the authors find the CAPS proprietary star ratings are also quite successful at identifying a good set of stocks [3] and therefore their results are consistent with our findings. Our method allows us to gain confidence in an individual experts' votes by evaluating their historical vote performance, as well as confidence in the stock picks by having many people vote with more experts-thereby enabling us to pick the optimal crowd.. In Figure 11 we see that when we use only 1 expert, relatively few stocks are voted on more than once, in contrast to when we use 250 experts. There is a tension between the two-experts and crowd-however, because ultimately even though a crowd of size 250 outperforms the S&P 500 but performs less well than the optimized strategy, the entire crowd should perform only about as well as the S&P 500. We plan to explore this tension with more experiments and with simulation. In addition, we would still like to rework the user scoring functionality once we have a better definition of expert-currently, we take the product of the voter's performance over time, which has the effect of greatly favoring the most prolific pickers.. In a real-world setting, this strategy likely would be integrated with other quantitative investing strategies. Given that this is applied machine learning research, it is important to consider the results in a business context. To that end, several important factors related to how these results would translate into real money are summarized below. We invest in a large number of stocks, frequently in the range of 50-100. The long-short split is close to the overall over/under split, which is to say that about one-fourth to one-fifth are long and the rest are short. The combination of a moderately large short component and a large number of stocks with a very short rebalancing period (typically 1 day, of course, and sometimes up to 3 or 4) means that transaction costs would be significant in practice.. As the holding period is fairly short, there are also tax implications. A significant component of the strategy is short, which likely will result in borrowing fees from the brokerage. There may be insufficient liquidity or volume in some of the securities traded. This strategy is tested in a universe of 7,142 stocks. Given that the S&P 500 often is considered a good approximation of the market, this universe clearly includes a large number of microcap stocks. Depending on the size of the portfolio and the specific positions selected, the markets for some of these securities may not be large enough to support the level of trading that the strategy relies on. These caveats certainly do not invalidate the results shown; they merely serve to remind us that there may be important differences between theory and practice. To test our two hypotheses-(1) stock picks based on the entire crowd-based wisdom will outperform the S&P 500 and (2) stock picks based on the picks of experts, identified by our expert ranking approach, will outperform using the entire crowd to vote-we used a GA to identify an investment strategy relying on a blend of expert-based wisdom and crowd-based wisdom. We then compared the performance of this strategy to two baseline strategies, the first driven solely by the crowd-by the S&P 500-and the second ranking people only on their past two stock picks. In addition to the baselines we used for comparison, we ran two very important sanity checks, which we discuss later. Finally, we compared our strategy to two methods discussed in the literature, one from computer science and one from finance. Our approach includes both a ranking component to identify experts and an investment component. From now on, we will refer to these two components either in part or together as the custom strategy. In broad terms, our custom-custom-blended strategy was designed as follows.. Our custom stock-picking strategy consisted of several parts. First, we ranked all the users (according to a metric described later in this paper). Then, we took the top A expert users and invested in them all equally. (It is important to note that this investment only occurred after a delay of one day, in order to ensure that a real-world implementation would be able to acquire the necessary picks before the market opened the following day.) That is, we invested 1/A of the portfolio in each user's picks, with the portion of the portfolio assigned to a given user being distributed evenly through all of the stocks picked by that user. We re-ranked the users every B days; then, we reinvested the portfolio according to the new ranking.. Our user-ranking methodology scored users based on the performance of their picked stocks. We looked at all of the picks made by a user over the past C days, then looked at the return of those stocks over a holding period of D days. A user's score was the product of the returns of the user's picked stocks over the C-day period, with the inverse return being used for stocks picked to underperform. Thus, there were four parameters to our strategy, which are summarized as follows: A = Number of experts-the number of top-ranked users whose picks we considered; B = Portfolio holding period-the period of portfolio re-balancing and user re-ranking; C = User test period-users' picks during this period influenced their score; D = User test holding period-users' picked stock returns were calculated over this time period. When we learn a set of parameters (A,B,C,D) on a training data set, we call those optimized parameters opt-CAPS for that training set.. To better understand our methodology, consider the following example: Suppose our parameter values are A = 5, B = 3, C = 10, D = 2. Every 3 days (the portfolio holding period), we rank all the users, then look at the top 5 (the number of experts). We then invest 20 percent of the portfolio in each of the users. Suppose, for example, that one day the top user picked 5 stocks, and the second-best user picked 10. In this case, we would invest 4 percent of the portfolio in each of the 5 stocks picked by the top user and 2 percent of the portfolio in each of the 10 stocks picked by the second-best user. If the two users had a pick in common, then we would invest 6 percent of the portfolio in that position. Also, we note that if a pick expects a stock to underperform, then we would take a short position on that stock.. With regard to ranking users in our example, we would consider a user's picks over the last 10 trading days (the user test period). Over the course of those 10 days, we would tally the returns of the user's picked stocks using holding periods of 2 days (the user test holding period). The user's score would then be the product of these returns-using inverse returns for underperforming picks. Naturally, we would not consider the stocks picked by a user yesterday, because, with a user test holding period of 2, the results of those picks could not be known until tomorrow.. We used a GA to learn the values for these four parameters, on our training period data, to maximize our fitness function-namely, the Sharpe ratio of an investment strategy over the training period. The Sharpe ratio is defined as. where R is the asset return, R f is the return on a benchmark asset (we use the risk-free rate of return [3 percent annualized, which is a close approximation of the long-term mean risk-free rate-the risk-free interest rate for investing money]), E[R -R f ] is the expected value of the excess of the asset return over the benchmark return, and σ is the standard deviation of the asset excess return [21] . Once the parameters are learned on the training period data set in one time period, we apply the learned model to test data drawn from a future time period.

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Interruption costs of service sector electricity customers, a hybrid approach A power outage brings in economic losses for both the customers and the utilities. Studying these unwanted events and making solid predictions about the outcomes of the interruptions has been an attractive area of interest for the researchers for the last couple of decades. By making use of a customer survey study conducted in Finland, this paper benefits from both the reported cost data collected from customers and from the analytical data that are available and then presents a new hybrid approach to estimate the customer interruption costs of service sector customer segment. Making use of Value Added information of the customers is a common practice for the cost normalization purposes. This paper verifies the approach by comparing the findings of the customer survey and the econometric model suggested here. This study is a unique source in terms of providing a reliable, easy to apply, and a straightforward model for calculating the economic impacts of power outages. Starting from 1980s many countries restructured their power sector putting aside non-competitive, monopolistic and regulated model. Along with this revolutionary change came the emphasis on the significance of electric power reliability and therefore its economic worth. The authorities, the utilities and surely the costumers are asking for continuous electric supply with a certain level of power quality. However discussions arise with the questions of ''What is the monetary worth of this?'' and ''Who is going to pay for it?'' The answer of the second question is not in the scope of this study. To answer the first question, there have been numerous studies done so far . However, there is no widely accepted methodology to come up with a credible and acknowledged solution to estimate the worth of electric power reliability yet. This makes studying the estimation of the electric power interruptions an attractive area of interest for the members of the electric power society.. The electric power customers could be divided into customer sectors of industrial, service (or commercial), residential, etc. regarding their power consumption characteristics. To make better estimations and to reach sector specific results, this paper focuses on estimating the costs of power outages for the service sector customers only. Before going through detailed analysis, understanding the nature of the power interruptions is compulsory. The interruptions could roughly be grouped into three types. Momentary interruptions, as the name calls, are the ones that last for a very short time, typically some seconds, or even less than 1 s. Sporadic interruptions, on the other hand, are caused by severe weather conditions such as floods, hurricanes or thunder storms. These types of interruptions pose great dangers for all the parties that benefit from the electric power system since they tend to last longer durations and they end up with quite high economic damages in the power infrastructure. The last type is the chronic interruptions. There are many factors that might end up with chronic interruptions. Insufficient power generation, faults in the power system due to aging or lack of maintenance, the faults resulted from power system operation or overloading of the system are of some examples that end up with an unwanted and unexpected interruption [22] . The duration of these interruptions might be from minutes to hours depending on the severity of the fault that occurred. Since the frequency of these interruptions is much higher than the others, in this paper, the authors focused only on the chronic interruptions and sought for a methodology to come up with credible and sound estimates about the economic consequences of these events.. To fully understand the results of the interruptions, the impacts caused by these events must be analysed and classified thoroughly. In the case study report of the 1977 New York blackout by the US Department of Energy the impacts of interruptions were grouped into two main categories: direct and indirect impacts [23] . The direct impacts include the direct effects of power cuts that cause economic losses such as sales loss, lost manufacturing, interruption of services, suspension of transportation, spoiled materials, damages on the electric equipment and on electronic data, other damages and accidents resulted from interruptions or, worst of all, injuries and deaths. The analysis of these events is relatively easy when they are compared to the indirect impacts. The indirect effects of power outages compose of arsons, looting, public disorder and crimes due to blackouts, possible sharp increases in the insurance rates, property losses, overtime payments, cancellation of social activities, lost tax revenues, the costs for recovering from looting and so on. When these indirect impacts are checked, it is obvious that the effects of some of those can only be seen after a considerable amount of time passes after the interruption. These long time effects make the analysis of the indirect impacts rather a challenging and a difficult task. The comprehensive report [23] and the significant study focusing on factors affecting customer outage costs [24] show that the economic worth of the indirect impacts can be much higher than that of direct ones. Nevertheless, as pointed out earlier, to analyse indirect economic results of the power interruptions is a tedious and demanding task, which can only be achieved by a large scale and extensive study that will take place after a major blackout event such as the infamous New York City blackout of 1977.. There are two main aspects when the customer interruption cost (CIC) assessments are done. The first matter is the methodology of collecting necessary input data and corresponding tools for the estimations and suggestions about the economic correspondence of the CICs. When the proper tool for the data collection is selected, another challenging point arises. How the raw data can and should be interpreted to get as bias free as possible results is the second phase of a complete and credible CIC analysis.. Section 'Customer interruption cost analysis approaches' of this paper presents different methodologies that are used to estimate the economic impacts of the power interruptions. Section 'The customer survey' includes the finnish service sector customer survey study and presents several customer damage functions (CDF) that are adopted throughout the paper. Moreover, the problem of strategic responses is pointed out and a remedy for zero and extreme responses is proposed. The hybrid model that combines the indirect analytical method and customer survey methodology is suggested in Section 'The hybrid model'. Comments, discussions and conclusions are summarized in Section 'Discussion and conclusions'. It is practically impossible to present a 100% credible and concern free power outage cost estimation tool with the current methodologies. Since understanding the economic worth of the electric power reliability is vitally important for power system planning, operation and electric power market structure, there has been increased emphasis on the customer interruption cost assessments for the last 30 years.. The customer survey method is the most popular and the most favoured tool for reliability worth assessments among the electric power society. On the other hand, there are some efforts to study the phenomena by following the indirect analytical methods by expressing the CIC directly using the Value Added (GDP) of a customer segment with the annual energy consumption [26] [27] [28] . It is clear that both approaches possess certain advantages and disadvantages. In this paper, the authors attempted to create a hybrid econometric approach that will benefit from both tools, and tried to minimize the shortcomings of these models. For each party of the electric power business; the authorities, the utilities and the customers, what is wanted for the CIC assessments is obvious: a method which is customer specific, bias free, easy to apply, not so time consuming and finally reasonably low-priced. This paper proposes the utilization of publicly declared and available Value Added data of the customers with a simple customer survey which is solely focused on the perishables in case of unwanted power interruptions. When the focus is only on the monetary correspondence of the perishables, then the SRF will be minimal since estimating the value of spoiled materials and dangers on the equipment is a straightforward task that even uneducated or ineligible personnel can achieve it easily without too much biases. Another benefit of this approach is that since the assessment of the perishables will be based on actual events, or experience, the survey results will be more reliable. Although suggested statistical elimination tool is successful in censoring the zero and extreme responses, the proposed methodology is the only way to cope with the strategic responses that lie in the uncensored region of data distribution. As a consequence, it can be claimed that the hybrid model is the most reliable way of calculating the economic worth of the power interruptions for service sector customers.. Momentary interruptions have been omitted in the analysis process because their nature in cost affecting is quite different than the other interruptions with longer durations. The authors believe that these outages should be studied along with another research that will focus on the economic worth of the power quality events. These events are not so significant for the service sector and for the domestic customers. However, they pose a serious danger on the manufacturing process for the industrial customers. The references [47] [48] [49] study the economic impacts of harmonics, voltage sags, flickers, transients, etc. and attempts to explain the consequences of such events. Moreover, one significant discussion over the CIC estimations is the frequency of the outages. In the current questionnaire format, the emphasis has been given on the duration of the outages. Nonetheless, by considering the restart losses and damages, it is comprehensible that the economic consequences of two interruptions with half an hour of duration each will be more severe than one interruption lasting one hour. Clearly certain modifications are needed in the customer surveys. Finally, when the literature is reviewed, it is seen that the majority of the studies are done for the industrial and service (commercial) sector customers [36] , while there are few studies only for the residential sector customers. The authors believe that further research is needed to come up credible interruption costs assessments for the residential sector as well.. The final crucial observation is that the concept of reliability worth is not fully covered by the interruption cost estimations done by customer surveys, analytical methods or by the hybrid method suggested here. The reason lies beneath the fact that only the direct impacts of the interruptions are dealt with via the above mentioned tools. However the reliability worth corresponds to all outcomes of both direct and indirect impacts. It is known that most of the indirect impacts are seen in the mid-term or in the longterm. The only way to identify these and then make a reliability worth analysis is to carry out an extensive case study which can only be done after major and rarely seen blackout events.. This paper is a unique work by distinguishing itself from the others by combining the widely adopted and popular model of customer surveys with an econometric model. That way it presents a novel hybrid approach that can be utilized to cope with the challenge of the customer interruption costs. The authors believe that the logic behind the hybrid approach is well-grounded and the reasoning is quite sound. The econometric model that makes use of the Value Added created by a customer group in a year is a practical way of estimating the costs of the planned outages. Nonetheless, proposed methodology for the unexpected outages can only be verified after another customer survey which will be designed solely on the costs of spoiled materials and damages for the service sector customers. In addition, since this study focuses on the interruption scenario that will yield the highest outage costs, it only covers winter afternoon outages and it excludes the effects of time of the day and season differences. That is why further extensive studies are needed to fully cover all interruption scenarios and all customer types.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


DisCons: a novel tool to quantify and classify evolutionary conservation of intrinsic protein disorder Background: Analyzing the amino acid sequence of an intrinsically disordered protein (IDP) in an evolutionary context can yield novel insights on the functional role of disordered regions and sequence element(s). However, in the case of many IDPs, the lack of evolutionary conservation of the primary sequence can hamper the study of functionality, because the conservation of their disorder profile and ensuing function(s) may not appear in a traditional analysis of the evolutionary history of the protein.Results: Here we present DisCons (Disorder Conservation), a novel pipelined tool that combines the quantification of sequence-and disorder conservation to classify disordered residue positions. According to this scheme, the most interesting categories (for functional purposes) are constrained disordered residues and flexible disordered residues. The former residues show conservation of both the sequence and the property of disorder and are associated mainly with specific binding functionalities (e.g., short, linear motifs, SLiMs), whereas the latter class correspond to segments where disorder as a feature is important for function as opposed to the identity of the underlying sequence (e.g., entropic chains and linkers). DisCons therefore helps with elucidating the function(s) arising from the disordered state by analyzing individual proteins as well as large-scale proteomics datasets. Conclusions: DisCons is an openly accessible sequence analysis tool that identifies and highlights structurally disordered segments of proteins where the conformational flexibility is conserved across homologs, and therefore potentially functional. The tool is freely available both as a web application and as stand-alone source code hosted at http://pedb.vib.be/discons. DisCons is a novel and freely available online and downloadable tool that combines the quantitative description of the position-specific evolutionary conservation of the amino acid sequence with predictions of the conservation of its disordered/flexible state, providing meaningful information on the evolutionary context of a disordered protein segment. Furthermore, DisCons uses this combined information to classify each disordered position into one of three categories, namely: constrained, flexible and non-conserved. These classes have been suggested to correlate with distinct functions that arise from the disordered state; therefore DisCons may provide information orthogonal to those obtained by other methods, which potentially enhances the reliability of the identification of functionally relevant disordered segments within proteins. We demonstrated that DisCons can be used to investigate both sequence-and disorder conservation in a functionally meaningful manner by bench-marking our procedure on MoRF and SLiM datasets, which are known to be conserved functional units enriched in structural disorder. It is important to emphasize that the success of calculation with DisCons strongly depends on the quality of the underlying multiple sequence alignment; therefore it is Figure 5 Example of a flexible disordered linker. The intrinsically disordered protein Sic1 binds to the CDC4 subunit of an SCF ubiquitin ligase forming a fuzzy complex (data for the structural ensemble obtained from the Protein Ensemble Database, PED). Two conformations of the same Sic1 fragment from the ensemble of this complex are displayed on panels A and B. Below, the DisCons profile on panel C corresponds to the same fragment, and shows the segment that is flexible disordered between two constrained disordered regions (1st MoRF and 2nd MoRF) that bind to Cdc4. This former segment corresponds to a flexible linker (shown in orange in panels A and B), which is found between two MoRFs (1st and 2nd on panels A and B) that bind to the same binding pocket in the fuzzy complex. The flexible conservation of the linker indicates that the sequence is of less importance than its conformational flexibility.. advised to review and optimize each MSA to maximize the information of the output. Taken this into consideration, DisCons can be used as an online or stand-alone tool for quantifying the conservation of both sequence and structural disorder by analyzing large-scale protein datasets and individual proteins. As such, DisCons might provide an additional layer of information for the investigation of protein disorder, and could serve to enhance the performance of prediction software such as MoRFPred [13] , or provide descriptive information for disorder related databases such as D2P2 [32] , MobiDB [33] or PED [31] . Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) within structured proteins are defined by the lack of a stable tertiary structure and a corresponding high degree of flexibility under physiological conditions [1] . The importance of conformational flexibility is reflected in the observation that IDPs and proteins with IDRs are often involved in essential cellular processes, such as cell-cycle regulation, transcription, and translation [2] [3] [4] . Additionally, they often play major roles in pathologies associated with aggregation and misfolding [5, 6] , making them attractive potential drug targets [7] . Genes encoding such amino acid sequences are under reduced selective pressure, which is manifest in a higher sequence diversity compared to genes of structured proteins/domains [8] . Whereas the functionality of a protein segment is often approached by investigating the evolutionary history of its primary sequence [9] , this is often difficult to achieve with IDP/IDR sequences, due to their generally high sequence diversity [10] .. On the other hand, combining the information derived from analyzing the conservation of both sequence and disorder can be much more useful, and this idea has recently been suggested to partition disordered residue positions into three separate groups of potentially different functional attributes: i) 'constrained' , if both features (amino acid sequence and the property of disorder) are conserved; ii) 'flexible' , if only disorder is conserved; and finally, iii) 'non-conserved' positions where disorder is not conserved. These specific evolutionary behaviors have been shown to correlate with distinct disorder-related functional categories [11] . In general, segments of constrained disorder are often associated with protein binding and molecular recognition, whereas flexible disorder is prevalent in linker segments acting as entropic chains. Nonconserved disorder has not been associated with specific protein function so far.. Here, we present DisCons, a novel web application and downloadable, stand-alone source code that offers a description of the conservation of both the amino acid sequence and of the feature of structural disorder, and performs the classification of disordered positions into these three categories. Thus, DisCons provides an additional (integrative) layer of information that together with other sequence-based tools, such as the PAML software package [12] , MoRFpred [13] and Anchor [14] , should facilitate the effective identification of functionally important disordered regions in proteins. Both the web application and the downloadable version of DisCons hosted at the website are freely available without registration. The DisCons website is divided into four functional sections that are accessible both through the menu and via the options shown on the welcome page. These sections correspond to the three running mode interfaces: 'quick', 'advanced' and 'from alignment'. The fourth functional section is 'help', which offers the complete documentation of the server and source code, in addition to a user guide.. The 'quick' running mode requires a single protein sequence (in FASTA format) as input, or alternately, a UniProt [15] accession ID. In this mode, the default parameters are used through all the calculation steps of the DisCons workflow. Although this calculation is the easiest to set up, experienced users might prefer to use the 'advanced' tool enabling a better understanding of the results leading to more fine-tuned functional interpretations.. The 'advanced' mode also accepts a single protein sequence or UniProt ID in a manner similar to the 'quick' calculation, but in this mode users can manually set all the parameters of the underlying calculation, allowing for a detailed optimization of the protocol pipeline, and a better overall command of the final results.. Finally, the 'from alignment' mode is best suited if the user already has a custom made, reliable multiple sequence alignment that can be used for the calculations. The main advantage of this mode is speed, since the need for running a BLAST search and constructing the multiple sequence alignment with MAFFT (which are the most time-consuming of all the steps) is circumvented. Therefore, this mode is significantly faster than the others which start from a single sequence (although even in 'quick' and 'advanced' modes, the approximate time for generating the results is 34 seconds for a~2400 residue long protein). By default, the stand-alone source code is also running 'from alignment'; however if the necessary dependencies, namely BLAST+ [16] and MAFFT [17] are available locally in the user's computer, the full pipeline can be utilized in a straightforward manner.. Depending on the running mode, the workflow of the calculations has a different starting point (Figure 1) . In 'quick' and 'advanced' modes, the procedure starts with a BLASTP or PSI-BLAST search [16] to collect sequences similar to the query sequence. In 'advanced' mode, the search dataset (Swiss-Prot [15] (used by default) or PDB [18] ) and the BLAST threshold values can be specified. Next, a multiple sequence alignment (MSA) is created from the set of identified homologous sequences using MAFFT [19] . This is the most crucial part of the procedure, since aligning disordered regions is non-trivial due to the potential diversity of related sequences. Because an incorrect alignment will compromise the subsequent calculations, it is advised either to use the 'advanced' mode to fine-tune the alignment procedure or to use a reliable, user-defined multiple alignment in the 'from alignment' mode.. In the next step (which is the starting point when running in the 'from alignment' mode), the MSA is used to construct an aligned disorder profile by running IUPred (default) [20] , VSL2 [21] , ESpritz [22] or FoldIndex [23] on each of the aligned sequences. The disorder scores are first transformed to a binary scale (1 = disordered, 0 = ordered; residues with a disorder score of 0.5 or greater are considered as disordered) for each sequence. Details about the transformation procedure for the three different disorder predictors are given in the help section of the website. Next the fraction of disordered residues at every position in the MSA across the different sequences is calculated, thereby effectively quantifying the position-wise conservation of disorder.. Next, sequence conservation scores for each position in the alignment are calculated using the algorithm developed by Capra et al. [9] . In 'advanced' and 'from alignment' modes, a number of parameters such as the algorithm of choice or the background distribution can be specified to make the calculation more robust.. Finally, positions in the MSA are scored by both their sequence-and disorder conservation, ranging from 0 (very diverse) to 9 (highly conserved) ( Figure 1 ). Based on these pairs of scores, each position will fall into one of four distinct categories. As suggested by Bellay et al. [11] , positions with a higher degree of disorder can be 'constrained' ('C'), if both the sequence and disorder conservation scores are 5 or greater; 'flexible' ('F'), if the sequence conservation is lower than 5 but the disorder conservation is 5 or greater; or 'non-conserved' ('N'), if the disorder conservation is lower than 5, but higher than 0. Positions with a disorder conservation score of 0 are completely lacking disorder and therefore are considered as 'structured' ('S'). Thus, regions of constrained disorder show strong conservation both at the amino acid level, and also of the disorder feature, while flexible disordered regions are variable in terms of amino acid sequence, but retain a significant level of disorder in evolution. Lastly, non-conserved disordered regions lack disorder as a conserved feature, and are generally thought not to be associated with functions [11] .. On the results page, the position-specific conservation profile is provided on the output screen, and the fractions of residues falling into each of these distinct categories are also displayed in a tabular format at the bottom of the page, effectively quantifying the conservation of disorder in the query sequence (a part of such an output is shown in Figure 1 ). The sequences of consecutive 'constrained' disordered regions are also recorded and are available for download in FASTA format along with the profiles and fractions in text format using the links that are provided. Such segments of consecutive stretches of constrained disorder are most likely to correspond to functionally important IDRs such as linear motifs or MoRFs, as we describe below. . The procedure starts either from a query sequence, followed by a BLASTP or PSI-BLAST search and the aligning of the retrieved sequences, or from a user-provided multiple sequence alignment. Next, position-specific sequence-and disorder conservation scores are calculated and finally these pairwise scores are combined to create the (aligned) DisCons profile. The result of one such classification is displayed at the bottom of the figure.

O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Fuzzy Answer Sets Approximations Fuzzy answer set programming (FASP) is a recent formalism for knowledge representation that enriches the declarativity of answer set programming by allowing propositions to be graded. To now, no implementations of FASP solvers are available and all current proposals are based on compilations of logic programs into different paradigms, like mixed integer programs or bilevel programs. These approaches introduce many auxiliary variables which might affect the performance of a solver negatively. To limit this downside, operators for approximating fuzzy answer sets can be introduced: Given a FASP program, these operators compute lower and upper bounds for all atoms in the program such that all answer sets are between these bounds. This paper analyzes several operators of this kind which are based on linear programming, fuzzy unfounded sets and source pointers. Furthermore, the paper reports on a prototypical implementation, also describing strategies for avoiding computations of these operators when they are guaranteed to not improve current bounds. The operators and their implementation can be used to obtain more constrained mixed integer or bilevel programs, or even for providing a basis for implementing a native FASP solver. Interestingly, the semantics of relevant classes of programs with unique answer sets, like positive programs and programs with stratified negation, can be already computed by the prototype without the need for an external tool. Answer Set Programming (ASP), i.e., logic programming under stable model semantics (Gelfond and Lifschitz 1991) , is a declarative language for knowledge representation (Niemelä 1999; Marek and Truszczyński 1999; Lifschitz 2002) . In ASP, problems are modeled by specifying a set of requirements that all solutions, called answer sets, have to satisfy. One of the strengths of ASP is its capability to model non-monotonic knowledge, overcoming a well-known limitation of classical logic, which can only deal with monotonic inferences. While monotonicity is desired in mathematics, it is widely considered a weakness for knowledge representation (Baral 2003) , where non-monotonicity arises in common reasoning tasks such as reasoning by default, abductive reasoning and belief revision. ASP can handle these tasks naturally (Marek and Remmel 2004; Lin and You 2002; Delgrande et al. 2008) , allowing for modeling and reasoning on incomplete information, and possibly retracting some conclusions as new knowledge on the application domain is acquired. Since complete knowledge can only be achieved in mathematical abstraction, it can be stated that ASP makes logic closer to the real world.. However, ASP is still based on precise information, which cannot always be assumed in the real world. For example, measures provided by any instrument or sensor always come with some degree of tolerance, and information expressed in natural language are often vague: there is no precise way to distinguish persons that are tall from those who are not. Fuzzy logic (Dubois et al. 1991) can handle vague information of this kind by interpreting propositions with a truth degree in the interval of real numbers [0, 1]. Intuitively, the higher the degree assigned to a proposition, the more true it is, with the extreme elements 0 and 1 denoting totally false and totally true, respectively. Consider for example the Barber of Seville paradox: In the small town of Seville, all and only those men who do not shave themselves are shaved by the barber. Classical set theory can neither prove nor disprove that the barber shaves himself, hence a fuzzy interpretation of this proposition should be 1/2, the most undetermined truth degree.. Fuzzy Answer Set Programming (FASP) aims at combining ASP and fuzzy logic. For example, a FASP encoding of the Barber of Seville paradox is shaves(barber, X ) ← not shaves(X , X ) shaves(X , X ) ← not shaves(barber, X ) from which shaves(barber, barber) gets the expected truth degree 1/2. FASP has been defined for a very general framework (Nieuwenborgh et al. 2007b) , allowing several connectors to be combined in the same program. With the aim of providing an indication for implementing a FASP solver, more constrained frameworks have been considered by Lukasiewicz (2006) and Janssen et al. (2012) . However, current proposals are based on compilations into different paradigms and introduce many auxiliary variables which could affect performance negatively. The focus of this paper is on operators for approximating fuzzy answer sets. These operators can be used for limiting the search space of an external tool, such as the linear or bilevel program approaches proposed by Lukasiewicz and Janssen et al., or as the basis for implementing a native FASP solver.. More precisely, in this paper we describe operators for computing two fuzzy interpretations, called lower and upper bound, such that every answer set is between these bounds. We introduce fuzzy unfounded sets, which generalize the notion of unfounded sets of classical ASP to deal with fuzzy semantics. We also define a well-founded operator that combines the fuzzy T P operator with the complement of the greatest unfounded set to improve on previously computed bounds. We show that these operators yield answer sets for positive and stratified FASP programs in polynomial time, while in general produce the well-founded semantics by Damásio and Pereira (2001) . For dealing with normal FASP programs, we introduce the new minimal satisfiability operator S P . For semantics based on the Lukasiewicz t-norm, this operator is polynomial-time computable and can further tighten the approximation. Finally, we describe a prototypical implementation which combines optimization ideas from classical ASP solvers Lierler and Maratea 2004; Alviano et al. 2011) , and report on an experiment assessing the potential performance gain provided by our operators to a bilevel program solver. We studied the problem of finding answer sets for normal FASP programs with t-norm based semantics. We studied fuzzy variants of the operators T P and R P , which bound the class of all answer sets of a FASP program P . These operators, as well as the combined well-founded operator W P , extend well-known operators from classical ASP to handle fuzzy semantics. As such, our operators preserve many of the properties that make them suitable for practical implementations. In particular, we have shown that one application of W P requires only polynomial time, measured on the size of P . Moreover, for positive and stratified programs, an iterative application of this operator yields the unique answer set in polynomial time, independently of the t-norm used. For normal FASP programs, which may have none or infinitely many answer sets, this operator is only guaranteed to provide lower and upper approximations for the class of answer sets. Depending on the program and the t-norm used, better bounds can be achieved by combining R P with a new operator S P . In particular, S P is computable in polynomial time for the Lukasiewicz t-norm by solving at most one linear program for each atoms in the program.. We implemented a prototype which applies the operators in an optimized manner, using S P only when no information can be obtained from W P . In particular, if the program is stratified, then S P will never be triggered. The system also keeps track of previous solutions of the set of inequalities introduced by S P , to avoid trying to optimize atoms whose current bounds are already known to be optimal. It also takes advantage of other optimizations developed for classical ASP, such as source pointers, to reduce the number of computations needed. The approximation provided by our prototype could aid in the computation of fuzzy answer sets, as evidenced by our experiment.. There are several lines for future work. From the theoretical point of view, we plan to investigate further conditions and operators that allow a precise computation of answer sets. In particular, as finding answer sets for normal FASP programs is NP-hard, we need to develop methods for efficiently dealing with a choice operator. We believe that the completion approach by Janssen et al. (2012) can be improved through the introduction of binary selection variables. From the practical point of view, we intend to improve the prototype, which currently relies on an external tool for computing answer sets when the bounds cannot be further improved. One idea for this point would be to implement a completion-based method extended with learning techniques. The study of fuzzy extensions of logic programs can be traced back more than two decades (see e.g. Dubois et al. 1991) . Moreover, compared to classical logic, fuzzy logics offer several additional levels of liberty for the definition of their semantics; namely, the choice of the space of truth degrees, the interpretation of the conjunction, the negation, and even the implication. Hence, we describe only the work that is closest related to ours. The first generalization of the immediate consequence operator to deal with fuzzy semantics was due to Achs and Kiss (1995) and Achs (1997) , albeit exclusively for the Gödel t-norm semantics. Fuzzy answer set semantics were introduced by Lukasiewicz (2006) based on a generalization of the Gelfond-Lifschitz transformation (Damásio and Pereira 2001) . It was then shown that positive and stratified programs have a unique answer set, and that it can be obtained by a finite iteration of the T P operator. However, no further analysis on the number of iterations needed to obtain that answer set was made. It is worth noting that the semantics described by Lukasiewicz are based on a finite set of truth values, rather than the whole interval [0, 1], as in our case. Nonetheless, the proof ideas can be generalized to arbitrary t-norms over [0, 1] without difficulty. General fuzzy answer set programs have been studied in detail in the last years (Janssen 2011), considering not only general t-norm semantics, but also arbitrary connectives to be used in the head and the body of the rules.. While the complexity of finding fuzzy answer sets is now relatively well understood, there were to-date no solvers available. In an effort to compute answer sets for general programs, a completion method was proposed by Janssen et al. (2012) . The idea is to transform the program P into a set of fuzzy logic formulas, whose models correspond precisely to the answer sets of P . However, due to the lack of (optimized) fuzzy logic reasoners, this reduction does not allow for an effective implementation. A different approach, specifically designed for the Lukasiewicz t-norm, is to reduce the program P to a bilevel linear programming problem (Blondeel et al. 2012 ). This method, which is also implemented by our system, has important theoretical repercussions, e.g., it can be used to show that disjunctions on rule heads do not add expressivity under this semantics.. Unfounded sets for FASP programs were first defined by Nieuwenborgh et al. (2007a) ; these unfounded sets are defined w.r.t. total interpretations and are actually crisp sets used for characterizing fuzzy answer sets. We will refer to this notion as crisp unfounded sets. Our definition is more general: it is given w.r.t. partial interpretations and for fuzzy sets; it is suitable for pruning the search space but also characterizes answer sets. A relationship between the two notions follows by Theorem 4. To the best of our knowledge, there had been no previous attempts to generalize the R P operator as a complementation of unfounded sets. This operator allows for a better approximation of answer sets without spending too many resources. A similar idea was studied by Loyer and Straccia (2009) , where a well-founded semantics is used for querying fuzzy logic programs over the Gödel t-norm. A well-founded semantics was also defined by Damásio and Pereira (2001) , for which we can prove the following result.

O O O O O B I I O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O


Locking Protocols for Materialized Aggregate Join Views The maintenance of materialized aggregate join views is a well-studied problem. However, to date the published literature has largely ignored the issue of concurrency control. Clearly immediate materialized view maintenance with transactional consistency, if enforced by generic concurrency control mechanisms, can result in low levels of concurrency and high rates of deadlock. While this problem is superficially amenable to well-known techniques such as finegranularity locking and special lock modes for updates that are associative and commutative, we show that these previous techniques do not fully solve the problem. We extend previous high concurrency locking techniques to apply to materialized view maintenance, and show how this extension can be implemented even in the presence of indices on the materialized view. Although materialized view maintenance has been wellstudied in the research literature [GM99] , with rare exceptions, to date that published literature has ignored concurrency control. In fact, if we use generic concurrency control mechanisms, immediate materialized aggregate join view maintenance becomes extremely problematic  the addition of a materialized aggregate join view can introduce many lock conflicts and/or deadlocks that did not arise in the absence of this materialized view.. As an example of this effect, consider a scenario in which there are two base relations: the lineitem relation, and the partsupp relation, with the schemas lineitem (orderkey, partkey) (and possibly some other attributes), and partsupp (partkey, suppkey). Suppose that in transaction T 1 some customer buys items p 11 and p 12 in order o 1 , which will cause the tuples (o 1 , p 11 ) and (o 1 , p 12 ) to be inserted into the lineitem relation. Also suppose that concurrently in transaction T 2 another customer buys items p 21 Suppose now that we create a materialized aggregate join view suppcount to provide quick access to the number of parts ordered from each supplier, defined as follows:. create aggregate join view suppcount as select p.suppkey, count(*) from lineitem l, partsupp p where l.partkey=p.partkey group by p.suppkey; Now both transactions T 1 and T 2 must update the materialized view suppcount. Since both T 1 and T 2 update the same pair of tuples in suppcount (the tuples for suppliers s 1 and s 2 ), there are now potential lock conflicts. To make things worse, suppose that T 1 and T 2 request their exclusive locks on suppcount in the following order: (1) T 1 requests a lock for the tuple whose suppkey=s 1 . (2) T 2 requests a lock for the tuple whose suppkey=s 2 . (3) T 1 requests a lock for the tuple whose suppkey=s 2 . (4) T 2 requests a lock for the tuple whose suppkey=s 1 . Then a deadlock will occur.. The danger of this sort of deadlock is not necessarily remote. Suppose there are R suppliers, m concurrent transactions, and that each transaction represents a customer buying items randomly from r different suppliers. Then according to [GR93, , if mr<<R, the probability that any particular transaction deadlocks is approximately (m-1)(r-1) 4 The V locking protocol is designed to support concurrent, immediate updates of materialized aggregate join views without engendering the high lock conflict rates and high deadlock rates that could result if two-phase locking with S and X lock modes were used. This protocol borrows from the theory of concurrency control for associative and commutative updates, with the addition of a short-term W lock to deal with insertion anomalies that result from some special properties of materialized view updates. Perhaps surprisingly, due to the interaction between locks on base relations and locks on the materialized view, this locking protocol, designed for concurrent update of aggregates, also supports direct propagate updates to (non-aggregate) join views.. It is an open question whether or not immediate updates with serializable semantics are a good idea in the context of materialized views. Certainly there are advantages to deferred updates, including potential efficiencies from the batching of updates and shorter path lengths for transactions that update base relations mentioned in materialized views. However, these efficiencies must be balanced against the semantic uncertainty and the "stale data" problems that may result when materialized views are not "in synch" with base data. The best answer to this question will only be found through a thorough exploration of how well both approaches (deferred and immediate) can be supported; it is our hope that the techniques in this paper can contribute to the discussion in this regard.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O